From 33ea96be959d79179f59ad1acf907c207742fc56 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 10 Jul 2017 18:04:00 -0400
Subject: [PATCH 01/59] adding stan_CARbym; print/summary methods work

---
 DESCRIPTION           |   2 +-
 NAMESPACE             |   2 +
 R/print-and-summary.R |   3 +-
 R/stan_CARbym.R       |  73 ++++++++++++++++++
 R/stan_spatial.fit.R  | 168 ++++++++++++++++++++++++++++++++++++++++++
 exec/spatial.stan     | 118 +++++++++++++++++++++++++++++
 6 files changed, 364 insertions(+), 2 deletions(-)
 create mode 100644 R/stan_CARbym.R
 create mode 100644 R/stan_spatial.fit.R
 create mode 100644 exec/spatial.stan

diff --git a/DESCRIPTION b/DESCRIPTION
index 5aaa87a1e..516cb2009 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -59,4 +59,4 @@ LazyData: true
 NeedsCompilation: yes
 URL: https://groups.google.com/forum/#!forum/stan-users, http://mc-stan.org/
 BugReports: https://github.com/stan-dev/rstanarm/issues
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1
diff --git a/NAMESPACE b/NAMESPACE
index e4492b765..962f178a4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -93,6 +93,7 @@ export(product_normal)
 export(ranef)
 export(se)
 export(sigma)
+export(stan_CARbym)
 export(stan_aov)
 export(stan_betareg)
 export(stan_betareg.fit)
@@ -110,6 +111,7 @@ export(stan_lm.wfit)
 export(stan_lmer)
 export(stan_polr)
 export(stan_polr.fit)
+export(stan_spatial.fit)
 export(student_t)
 export(waic)
 if(getRversion()>='3.3.0') importFrom(stats, sigma) else
diff --git a/R/print-and-summary.R b/R/print-and-summary.R
index 77c586b29..6242f6e97 100644
--- a/R/print-and-summary.R
+++ b/R/print-and-summary.R
@@ -92,6 +92,8 @@ print.stanreg <- function(x, digits = 1, ...) {
       cut_mat <- mat[, cut_nms, drop = FALSE]
       cut_estimates <- .median_and_madsd(cut_mat)
     }
+    if ("car" %in% class(x))
+        nms <- setdiff(rownames(x$stan_summary), c("log-posterior", paste0("psi[", 1:length(x$y), "]")))
     ppd_nms <- grep("^mean_PPD", nms, value = TRUE)
     nms <- setdiff(nms, ppd_nms)
     coef_mat <- mat[, nms, drop = FALSE]
@@ -223,7 +225,6 @@ summary.stanreg <- function(object, pars = NULL, regex_pars = NULL,
                             probs = NULL, ..., digits = 1) {
   mer <- is.mer(object)
   pars <- collect_pars(object, pars, regex_pars)
-  
   if (!used.optimizing(object)) {
     args <- list(object = object$stanfit)
     if (!is.null(probs)) 
diff --git a/R/stan_CARbym.R b/R/stan_CARbym.R
new file mode 100644
index 000000000..5a20287f2
--- /dev/null
+++ b/R/stan_CARbym.R
@@ -0,0 +1,73 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2013, 2014, 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+#' Bayesian CAR BYM models via Stan
+#'
+#' Spatial regression modeling with a conditional autoregressive (CAR).
+#'
+#' @export
+#' 
+
+stan_CARbym <- function(formula,
+                        family = c("binomial", "poisson", "gaussian"),
+                        data,
+                        trials = NULL,
+                        W,
+                        ...,
+                        prior = normal(), prior_intercept = normal(),
+                        prior_PD = FALSE,
+                        algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                        adapt_delta = NULL,
+                        QR = FALSE) {
+  stan_function <- "stan_CARbym"
+  if (!requireNamespace("CARBayes", quietly = TRUE))
+    stop("Please install the CARBayes package before using 'stan_CARbym.")
+  mc <- match.call(expand.dots = FALSE)
+  algorithm <- match.arg(algorithm)
+  family <- match.arg(family)
+  mf <- model.frame(mc, data)
+  Y <- array1D_check(model.response(mf, type = "any"))
+  X <- model.matrix(formula, data)
+  stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
+                              trials = trials,
+                              family = family,
+                              stan_function,
+                              ...,
+                              prior = prior,
+                              prior_intercept = prior_intercept,
+                              prior_PD = prior_PD,
+                              algorithm = algorithm, adapt_delta = adapt_delta, 
+                              QR = QR)
+  if (family == "binomial")
+    family <- binomial(link = "logit")
+  else if (family == "poisson")
+    family <- poisson(link = "log")
+  else if (family == "gaussian")
+    family <- gaussian(link = "identity")
+  fit <- nlist(stanfit,
+               algorithm,
+               data,
+               x = X, y = Y,
+               family,
+               formula,
+               model = mf, 
+               call = match.call(),
+               stan_function = "stan_CARbym")
+  out <- stanreg(fit)
+  browser()
+  structure(out, class = c("stanreg", "car"))
+}
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
new file mode 100644
index 000000000..dbd6d6088
--- /dev/null
+++ b/R/stan_spatial.fit.R
@@ -0,0 +1,168 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2013, 2014, 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+#' @export
+#' 
+
+stan_spatial.fit <- function(x, y, w,
+                             trials = NULL,
+                             family = c("binomial", "poisson", "gaussian"),
+                             stan_function,
+                             ...,
+                             prior = normal(), prior_intercept = normal(),
+                             prior_PD = FALSE,
+                             algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                             adapt_delta = NULL,
+                             QR = FALSE) {
+  # check that W is appropriate
+  
+  algorithm <- match.arg(algorithm)
+  family <- match.arg(family)
+  
+  if (family == "gaussian") {
+    y_real <- y
+    y_int <- rep(0, length(y))
+    link <- "identity"
+    trials <- rep(0, length(Y))
+    family_num <- 1
+  }
+  else {
+    y_real <- rep(0, length(y))
+    y_int <- y
+    if (family == "binomial") {
+      link <- "logit"
+      family_num <- 3
+    }
+    else {  # poisson
+      link <- "log"
+      trials <- rep(0, length(Y))
+      family_num <- 2
+    }
+  }
+  
+  if (stan_function == "stan_CARbym")
+    mod <- 1
+  else if(stan_function == "stan_CARleroux")
+    mod <- 2
+  
+  sparse <- FALSE
+  x_stuff <- center_x(x, sparse)
+  for (i in names(x_stuff)) # xtemp, xbar, has_intercept
+    assign(i, x_stuff[[i]])
+  nvars <- ncol(xtemp)
+
+  ok_dists <- nlist("normal", student_t = "t", "cauchy")
+  ok_intercept_dists <- ok_dists
+
+  prior_stuff <- handle_glm_prior(prior, nvars, link, default_scale = 2.5, 
+                                  ok_dists = ok_dists)
+  for (i in names(prior_stuff)) # prior_{dist, mean, scale, df, autoscale}
+    assign(i, prior_stuff[[i]])
+  
+  prior_intercept_stuff <- handle_glm_prior(prior_intercept, nvars = 1, 
+                                            default_scale = 10, link = link,
+                                            ok_dists = ok_intercept_dists)
+  names(prior_intercept_stuff) <- paste0(names(prior_intercept_stuff), 
+                                         "_for_intercept")
+  for (i in names(prior_intercept_stuff)) # prior_{dist, mean, scale, df, autoscale}_for_intercept
+    assign(i, prior_intercept_stuff[[i]])
+  
+  # QR decomposition for both x and z
+  if (QR) {
+    if (nvars <= 1)
+      stop("'QR' can only be specified when there are multiple predictors.")
+    else {
+      cn <- colnames(xtemp)
+      decomposition <- qr(xtemp)
+      sqrt_nm1 <- sqrt(nrow(xtemp) - 1L)
+      Q <- qr.Q(decomposition)
+      R_inv <- qr.solve(decomposition, Q) * sqrt_nm1
+      xtemp <- Q * sqrt_nm1
+      colnames(xtemp) <- cn
+      xbar <- c(xbar %*% R_inv) 
+    }
+  }
+  
+  # pull out adjacency pairs from W
+  adj_fun <- function(W) {
+    W[upper.tri(W)] <- NA
+    out <- which(W == 1, arr.ind = TRUE)
+    return(out)
+  }
+  edges <- adj_fun(W)
+  
+  # need to use uncentered version
+  standata <- nlist(N = nrow(xtemp),
+                    K = ncol(xtemp),
+                    edges = edges,
+                    E_n = nrow(edges),
+                    family = family_num,
+                    X = if (has_intercept) x[,-1] else x ,  # use xtemp
+                    y_real = y_real,
+                    y_int = y_int,
+                    trials = trials,
+                    shape_tau = 1,
+                    shape_sigma = 1,
+                    shape_nu = 1,
+                    scale_tau = 1,
+                    scale_sigma = 1,
+                    scale_nu = 1,
+                    loc_beta = rep(0,ncol(xtemp)),
+                    scale_beta = rep(1,ncol(xtemp)),
+                    loc_alpha = 0,
+                    scale_alpha = 1,
+                    has_intercept = has_intercept,
+                    mod = mod)
+  
+  pars <- c(if (has_intercept) "alpha", "beta", if(mod == 1) "sigma", "tau", if(family == "gaussian") "nu",
+            "mean_PPD", if (mod < 2) "psi")
+  
+  stanfit <- stanmodels$spatial
+
+  if (algorithm == "sampling") {
+    sampling_args <- set_sampling_args(
+      object = stanfit, 
+      prior = prior, 
+      user_dots = list(...), 
+      user_adapt_delta = adapt_delta, 
+      data = standata, 
+      pars = pars, 
+      show_messages = FALSE)
+    stanfit <- do.call(sampling, sampling_args)
+  }
+  else {
+    stop(paste("algorithm", algorithm, "is not supported."))
+  }
+  check_stanfit(stanfit)
+
+  if (QR) {
+    thetas <- extract(stanfit, pars = "beta", inc_warmup = TRUE, 
+                      permuted = FALSE)
+    betas <- apply(thetas, 1:2, FUN = function(theta) R_inv %*% theta)
+    end <- tail(dim(betas), 1L)
+    for (chain in 1:end) for (param in 1:nrow(betas)) {
+      stanfit@sim$samples[[chain]][[has_intercept + param]] <-
+        if (ncol(xtemp) > 1) betas[param, , chain] else betas[param, chain]
+    } 
+  }
+  new_names <- c(if (has_intercept) "(Intercept)", 
+                 colnames(xtemp), "tau2", if(mod == 1) "sigma2",
+                 if(family == "gaussian") "nu2", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
+  stanfit@sim$fnames_oi <- new_names
+  browser()
+  return(structure(stanfit))  # return(structure(stanfit, prior.info = prior_info))
+}
diff --git a/exec/spatial.stan b/exec/spatial.stan
new file mode 100644
index 000000000..a63df5853
--- /dev/null
+++ b/exec/spatial.stan
@@ -0,0 +1,118 @@
+// CAR (BYM version i.e. IAR)
+data {
+  int<lower=0> N;                 // number of regions
+  int<lower=0> K;                 // number of predictors (inc intercept)
+  matrix[N,K] X;                  // model matrix
+  int<lower=0> trials[N];         // binomial trials (0 1d array if not applicable)
+  int y_int[N];                   // outcome
+  real y_real[N];                 // outcome
+  int<lower=1,upper=3> family;    // family (1 = Gaussian, 2 = Poisson, 3 = Binomial)
+  int E_n;                        // number of adjacency pairs
+  int edges[E_n, 2];              // adjacency pairs
+  real loc_beta[K];               // priors
+  real<lower=0> scale_beta[K];    // priors
+  real<lower=0> shape_tau;        // priors
+  real<lower=0> scale_tau;        // priors
+  real<lower=0> shape_sigma;      // priors
+  real<lower=0> scale_sigma;      // priors
+  real<lower=0> shape_nu;         // priors
+  real<lower=0> scale_nu;         // priors
+  real<lower=0> loc_alpha;        // priors
+  real<lower=0> scale_alpha;      // priors
+  int<lower=0,upper=1> has_intercept;
+  int<lower=1,upper=2> mod;
+}
+transformed data {
+  real poisson_max = pow(2.0, 30.0);
+}
+parameters {
+  real alpha[has_intercept];  // intercept
+  vector[K] beta;             // predictors on covariates (including intercept)
+  vector[N] theta_raw[mod == 1? 1 : 0];        // used for random effect (non-spatial)
+  vector[N-1] phi_raw;        // used for random effect (spatial)
+  real<lower=0> tau;          // variance i.e. tau^2
+  real<lower=0> sigma;        // variance i.e. sigma^2
+  real<lower=0> nu[family == 1? 1 : 0];  // applies only if family is gaussian
+}
+transformed parameters {
+  vector[N] theta[mod == 1? 1 : 0];        // non-centered random effect (non-spatial)
+  vector[N] phi;          // non-centered random effect (spatial)
+  theta[1] = sqrt(sigma) * theta_raw[1];
+  phi[1:(N - 1)] = phi_raw;
+  phi[N] = -sum(phi_raw);
+  phi = phi * sqrt(tau);
+}
+model {
+  vector[N] eta;   // linear predictor + spatial random effects
+  // model
+  if (has_intercept == 1) {
+    if (mod == 1)
+      eta = alpha[1] + X * beta + phi + theta[1];
+    else if (mod == 2)
+      eta = alpha[1] + X * beta + phi;
+  }
+  else {
+    if (mod == 1)
+      eta = X * beta + phi + theta[1];
+    else if (mod == 2)
+      eta = X * beta + phi;
+  }
+  if (family == 1) {
+    target+= normal_lpdf(y_real | eta, nu[1]);
+    target+= inv_gamma_lpdf(nu[1] | shape_nu, scale_nu);
+  }
+  else if (family == 2) {
+    target+= poisson_log_lpmf(y_int | eta);
+  }
+  else if (family == 3) {
+    target+= binomial_lpmf(y_int | trials, inv_logit(eta));
+  }
+  // priors
+  if (mod == 1)
+    target+= - N * log(sqrt(tau)) - 0.5 * inv(tau) * dot_self(phi[edges[,1]] - phi[edges[,2]]);
+  else if (mod == 2)
+    reject("leroux model not supported")
+  if (has_intercept == 1)
+    target += normal_lpdf(alpha | loc_alpha, scale_alpha);
+  target+= normal_lpdf(beta | loc_beta , scale_beta);
+  target+= normal_lpdf(theta_raw[1] | 0, 1);
+  target+= normal_lpdf(phi_raw | 0, 1);
+  target+= inv_gamma_lpdf(tau | shape_tau, scale_tau);
+  target+= inv_gamma_lpdf(sigma | shape_sigma, scale_sigma);
+}
+generated quantities {
+  real mean_PPD = 0;
+  vector[N] psi;
+  if (mod == 1)
+    psi = theta[1] + phi;
+  else if (mod == 2)
+    psi = phi;
+  {
+    vector[N] eta;
+    if (has_intercept == 1) {
+      if (mod == 1)
+        eta = alpha[1] + X * beta + phi + theta[1];
+      else if (mod == 2)
+        eta = alpha[1] + X * beta + phi;
+    }
+    else {
+      if (mod == 1)
+        eta = X * beta + phi + theta[1];
+      else if (mod == 2)
+        eta = X * beta + phi;
+    }
+    for (n in 1:N) {
+      if (family == 1)
+        mean_PPD = mean_PPD + normal_rng(eta[n], nu[1]);
+      else if (family == 2) {
+        if (eta[n] < poisson_max)
+          mean_PPD = mean_PPD + poisson_log_rng(eta[n]);
+        else
+          mean_PPD = mean_PPD + normal_rng(eta[n], sqrt(eta[n]));
+      }
+      else if (family == 3)
+        mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
+    }
+  }
+  mean_PPD = mean_PPD / N;
+}

From 6feee4f0d8722922bd225d4e425455a037900474 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 10 Jul 2017 20:22:56 -0400
Subject: [PATCH 02/59] get important post-estimation methods/functions working

---
 R/log_lik.R           | 18 +++++++++++++++---
 R/posterior_predict.R | 13 +++++++++++--
 R/stan_CARbym.R       | 15 ++++++++-------
 R/stan_spatial.fit.R  |  1 -
 R/stanreg.R           | 15 ++++++++++++++-
 5 files changed, 48 insertions(+), 14 deletions(-)

diff --git a/R/log_lik.R b/R/log_lik.R
index fba22e985..2a2c7059d 100644
--- a/R/log_lik.R
+++ b/R/log_lik.R
@@ -153,7 +153,10 @@ ll_args <- function(object, newdata = NULL, offset = NULL,
         trials <- 1
         if (is.factor(y)) 
           y <- fac2bin(y)
-        stopifnot(all(y %in% c(0, 1)))
+        if (!is(object, "car"))
+          stopifnot(all(y %in% c(0, 1)))
+        else
+          trials <- object$trials
       }
       data <- data.frame(y, trials, x)
     }
@@ -218,7 +221,11 @@ ll_args <- function(object, newdata = NULL, offset = NULL,
     data <- cbind(data, as.matrix(z))
     draws$beta <- cbind(draws$beta, b)
   }
-  
+  if (is(object, "car")) {
+    psi_indx <- grep("psi", colnames(stanmat))
+    psi <- stanmat[, psi_indx, drop = FALSE]
+    data$psi <- t(psi)
+  }
   nlist(data, draws, S = NROW(draws$beta), N = nrow(data))
 }
 
@@ -247,11 +254,16 @@ ll_args <- function(object, newdata = NULL, offset = NULL,
 }
 
 .xdata <- function(data) {
-  sel <- c("y", "weights","offset", "trials")
+  if (!is.null(data$psi))
+    sel <- c("y", "weights","offset", "trials", "psi")
+  else
+    sel <- c("y", "weights","offset", "trials")
   data[, -which(colnames(data) %in% sel)]
 }
 .mu <- function(data, draws) {
   eta <- as.vector(linear_predictor(draws$beta, .xdata(data), data$offset))
+  if (!is.null(data$psi))
+    eta <- eta + as.vector(data$psi)
   draws$f$linkinv(eta)
 }
 
diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index e306e6698..de5a77375 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -169,7 +169,8 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
   }
   if (!is(object, "polr") && is.binomial(family(object)$family))
     ppargs$trials <- pp_binomial_trials(object, newdata)
-
+  if (is(object, "car") && is.binomial(family(object)$family))
+    ppargs$trials <- object$trials
   ppfun <- pp_fun(object)
   ytilde <- do.call(ppfun, ppargs)
   if (!is.null(newdata) && nrow(newdata) == 1L)
@@ -326,7 +327,15 @@ pp_eta <- function(object, data, draws = NULL) {
     beta <- stanmat[, seq_len(ncol(x)), drop = FALSE]
     if (some_draws)
       beta <- beta[samp, , drop = FALSE]
-    eta <- linear_predictor(beta, x, data$offset)
+    if (is(object, "car")) {
+      psi_indx <- grep("psi", colnames(stanmat))
+      psi <- stanmat[, psi_indx, drop = FALSE]
+      if (some_draws)
+        psi <- psi[samp, , drop = FALSE]
+      eta <- linear_predictor(beta, x, data$offset) + psi
+    }
+    else
+      eta <- linear_predictor(beta, x, data$offset)
   } else {
     stanmat <- as.matrix(object$stanfit)
     beta <- stanmat[, seq_len(ncol(x)), drop = FALSE]
diff --git a/R/stan_CARbym.R b/R/stan_CARbym.R
index 5a20287f2..7f53a8837 100644
--- a/R/stan_CARbym.R
+++ b/R/stan_CARbym.R
@@ -52,12 +52,6 @@ stan_CARbym <- function(formula,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
-  if (family == "binomial")
-    family <- binomial(link = "logit")
-  else if (family == "poisson")
-    family <- poisson(link = "log")
-  else if (family == "gaussian")
-    family <- gaussian(link = "identity")
   fit <- nlist(stanfit,
                algorithm,
                data,
@@ -67,7 +61,14 @@ stan_CARbym <- function(formula,
                model = mf, 
                call = match.call(),
                stan_function = "stan_CARbym")
+  if (family == "binomial") {
+    fit$family <- binomial(link = "logit")
+    fit$trials <- trials
+  }
+  else if (family == "poisson")
+    fit$family <- poisson(link = "log")
+  else if (family == "gaussian")
+    fit$family <- gaussian(link = "identity")
   out <- stanreg(fit)
-  browser()
   structure(out, class = c("stanreg", "car"))
 }
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index dbd6d6088..cec67c4ca 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -163,6 +163,5 @@ stan_spatial.fit <- function(x, y, w,
                  colnames(xtemp), "tau2", if(mod == 1) "sigma2",
                  if(family == "gaussian") "nu2", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
   stanfit@sim$fnames_oi <- new_names
-  browser()
   return(structure(stanfit))  # return(structure(stanfit, prior.info = prior_info))
 }
diff --git a/R/stanreg.R b/R/stanreg.R
index 9da6e4711..2d31884b5 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -23,6 +23,7 @@
 stanreg <- function(object) {
   opt <- object$algorithm == "optimizing"
   mer <- !is.null(object$glmod) # used stan_(g)lmer
+  is_car <- object$stan_function %in% c("stan_CARbym", "stan_CARleroux")
   stanfit <- object$stanfit
   family <- object$family
   y <- object$y
@@ -90,8 +91,13 @@ stanreg <- function(object) {
   
   # linear predictor, fitted values
   eta <- linear_predictor(coefs, x, object$offset)
+  if (is_car) {
+    psi_indx <- grep("psi", colnames(as.matrix(object$stanfit)))
+    psi <- as.matrix(object$stanfit)[,psi_indx]
+    psi <- unname(colMeans(psi))
+    eta <- eta + psi
+  }
   mu <- family$linkinv(eta)
-
   if (NCOL(y) == 2L) {
     # residuals of type 'response', (glm which does 'deviance' residuals by default)
     residuals <- y[, 1L] / rowSums(y) - mu 
@@ -149,6 +155,13 @@ stanreg <- function(object) {
     out$eta_z <- eta_z
     out$phi <- phi
   }
+  if (is_car) {
+    # CARBayes doesn't include the scale parameters in out$coefficients so not sure if we want to.
+    # exclude <- c(1:nvars, psi_indx, which(rownames(stan_summary) %in% c("mean_PPD", "log-posterior")))
+    # out$coefficients = c(coefs, stan_summary[-exclude, select_median(object$algorithm)])
+    out$psi <- psi
+    out$trials <- object$trials
+  }
   
   structure(out, class = c("stanreg", "glm", "lm"))
 }

From 6ad17ba47651b135661f70ec2fbb33471d3ef986 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 12 Jul 2017 23:53:58 -0400
Subject: [PATCH 03/59] adding stan_bym (inla version)

---
 NAMESPACE            |  3 +-
 R/priors.R           |  8 +++-
 R/stan_CARbym.R      | 14 ++++---
 R/stan_spatial.fit.R | 87 ++++++++++++++++++++++++++++++++++----------
 exec/spatial.stan    | 66 +++++++++++++--------------------
 5 files changed, 110 insertions(+), 68 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 962f178a4..e227c0792 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -53,6 +53,7 @@ S3method(vcov,stanreg)
 S3method(waic,stanreg)
 export(R2)
 export(VarCorr)
+export(beta)
 export(cauchy)
 export(compare_models)
 export(decov)
@@ -93,12 +94,12 @@ export(product_normal)
 export(ranef)
 export(se)
 export(sigma)
-export(stan_CARbym)
 export(stan_aov)
 export(stan_betareg)
 export(stan_betareg.fit)
 export(stan_biglm)
 export(stan_biglm.fit)
+export(stan_bym)
 export(stan_gamm4)
 export(stan_glm)
 export(stan_glm.fit)
diff --git a/R/priors.R b/R/priors.R
index e046193bb..0141cc483 100644
--- a/R/priors.R
+++ b/R/priors.R
@@ -516,7 +516,13 @@ R2 <- function(location = NULL, what = c("mode", "mean", "median", "log")) {
   list(dist = "R2", location = location, what = what, df = 0, scale = 0)
 }
 
-
+#' @rdname priors
+#' @export
+beta <- function(alpha = 2, beta = 2) {
+  validate_parameter_value(alpha)
+  validate_parameter_value(beta)
+  nlist(dist = "beta", alpha, beta)
+}
 
 
 # internal ----------------------------------------------------------------
diff --git a/R/stan_CARbym.R b/R/stan_CARbym.R
index 7f53a8837..1d4316726 100644
--- a/R/stan_CARbym.R
+++ b/R/stan_CARbym.R
@@ -17,25 +17,26 @@
 
 #' Bayesian CAR BYM models via Stan
 #'
-#' Spatial regression modeling with a conditional autoregressive (CAR).
+#' Spatial regression modeling with a conditional autoregressive (CAR) prior.
 #'
 #' @export
 #' 
 
-stan_CARbym <- function(formula,
+stan_bym <- function(formula,
                         family = c("binomial", "poisson", "gaussian"),
                         data,
                         trials = NULL,
                         W,
                         ...,
                         prior = normal(), prior_intercept = normal(),
+                        prior_sigma = normal(), prior_tau = beta(), prior_nu = NULL,
                         prior_PD = FALSE,
                         algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                         adapt_delta = NULL,
                         QR = FALSE) {
-  stan_function <- "stan_CARbym"
-  if (!requireNamespace("CARBayes", quietly = TRUE))
-    stop("Please install the CARBayes package before using 'stan_CARbym.")
+  stan_function <- "stan_bym"
+  if (!requireNamespace("INLA", quietly = TRUE))
+    stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
   family <- match.arg(family)
@@ -49,6 +50,7 @@ stan_CARbym <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
+                              prior_sigma = prior_sigma, prior_tau = prior_tau, prior_nu = prior_nu,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
@@ -60,7 +62,7 @@ stan_CARbym <- function(formula,
                formula,
                model = mf, 
                call = match.call(),
-               stan_function = "stan_CARbym")
+               stan_function = stan_function)
   if (family == "binomial") {
     fit$family <- binomial(link = "logit")
     fit$trials <- trials
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index cec67c4ca..79fff3ba9 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -24,6 +24,7 @@ stan_spatial.fit <- function(x, y, w,
                              stan_function,
                              ...,
                              prior = normal(), prior_intercept = normal(),
+                             prior_sigma = normal(), prior_tau = beta(), prior_nu = NULL,
                              prior_PD = FALSE,
                              algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                              adapt_delta = NULL,
@@ -54,9 +55,9 @@ stan_spatial.fit <- function(x, y, w,
     }
   }
   
-  if (stan_function == "stan_CARbym")
+  if (stan_function == "stan_icar")
     mod <- 1
-  else if(stan_function == "stan_CARleroux")
+  else if (stan_function == "stan_bym")
     mod <- 2
   
   sparse <- FALSE
@@ -64,15 +65,19 @@ stan_spatial.fit <- function(x, y, w,
   for (i in names(x_stuff)) # xtemp, xbar, has_intercept
     assign(i, x_stuff[[i]])
   nvars <- ncol(xtemp)
-
-  ok_dists <- nlist("normal", student_t = "t", "cauchy")
+  
+  # temporarily suppress certain prior options
+  # ok_dists <- nlist("normal", student_t = "t", "cauchy")
+  ok_dists <- nlist("normal")
   ok_intercept_dists <- ok_dists
 
+  # Deal with prior
   prior_stuff <- handle_glm_prior(prior, nvars, link, default_scale = 2.5, 
                                   ok_dists = ok_dists)
   for (i in names(prior_stuff)) # prior_{dist, mean, scale, df, autoscale}
     assign(i, prior_stuff[[i]])
   
+  # Deal with prior_intercept
   prior_intercept_stuff <- handle_glm_prior(prior_intercept, nvars = 1, 
                                             default_scale = 10, link = link,
                                             ok_dists = ok_intercept_dists)
@@ -81,6 +86,31 @@ stan_spatial.fit <- function(x, y, w,
   for (i in names(prior_intercept_stuff)) # prior_{dist, mean, scale, df, autoscale}_for_intercept
     assign(i, prior_intercept_stuff[[i]])
   
+  # Deal with prior_tau and prior_sigma
+  if (stan_function == "stan_bym") {
+    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, link, default_scale = 1, 
+                                          ok_dists = "normal")
+    for (i in names(prior_sigma_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_sigma_stuff[[i]])
+    
+    prior_tau_stuff <- list(alpha = prior_tau$alpha, beta = prior_tau$beta)
+  }
+  else if (stan_function == "stan_icar") {
+    prior_tau_stuff <- list(alpha = 0, beta = 0)
+    prior_sigma_stuff <- list(prior_mean = 0, prior_scale = 0)
+  }
+  
+  # Deal with prior_nu
+  if (family == "gaussian") {
+    prior_nu_stuff <- handle_glm_prior(prior_nu, nvars = 1, link, default_scale = 1, 
+                                    ok_dists = "normal")
+    for (i in names(prior_nu_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_nu_stuff[[i]])
+  }
+  else {
+    prior_nu_stuff <- list(prior_mean = 0, prior_scale = 0)
+  }
+  
   # QR decomposition for both x and z
   if (QR) {
     if (nvars <= 1)
@@ -104,7 +134,7 @@ stan_spatial.fit <- function(x, y, w,
     return(out)
   }
   edges <- adj_fun(W)
-  
+
   # need to use uncentered version
   standata <- nlist(N = nrow(xtemp),
                     K = ncol(xtemp),
@@ -115,21 +145,40 @@ stan_spatial.fit <- function(x, y, w,
                     y_real = y_real,
                     y_int = y_int,
                     trials = trials,
-                    shape_tau = 1,
-                    shape_sigma = 1,
-                    shape_nu = 1,
-                    scale_tau = 1,
-                    scale_sigma = 1,
-                    scale_nu = 1,
-                    loc_beta = rep(0,ncol(xtemp)),
-                    scale_beta = rep(1,ncol(xtemp)),
-                    loc_alpha = 0,
-                    scale_alpha = 1,
+                    shape1_tau = c(prior_tau_stuff$alpha),
+                    shape2_tau = c(prior_tau_stuff$beta),
+                    loc_sigma = c(prior_sigma_stuff$prior_mean),
+                    scale_sigma = c(prior_sigma_stuff$prior_scale),
+                    loc_nu = c(prior_nu_stuff$prior_mean),
+                    scale_nu = c(prior_nu_stuff$prior_scale),
+                    loc_beta = as.array(prior_stuff$prior_mean),
+                    scale_beta = as.array(prior_stuff$prior_scale),
+                    loc_alpha = c(prior_intercept_stuff$prior_mean_for_intercept),
+                    scale_alpha = c(prior_intercept_stuff$prior_scale_for_intercept),
                     has_intercept = has_intercept,
                     mod = mod)
+  standata$X <- array(standata$X, dim = c(standata$N, standata$K))
+
+  # create scaling_factor a la Dan Simpson
+  create_scaling_factor <- function(dat, W) {
+    #The ICAR precision matrix (note! This is singular)
+    Q <-  diag(rowSums(W), dat$N) - W
+    #Add a small jitter to the diagonal for numerical stability (optional but recommended)
+    Q_pert <- Q + diag(dat$N) * max(diag(Q)) * sqrt(.Machine$double.eps)
+    
+    # Compute the diagonal elements of the covariance matrix subject to the 
+    # constraint that the entries of the ICAR sum to zero.
+    #See the function help for further details.
+    Q_inv <- inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
+    Q_inv <- as.matrix(Q_inv)
+    #Compute the geometric mean of the variances, which are on the diagonal of Q.inv
+    scaling_factor <- exp(mean(log(diag(Q_inv))))
+    return(scaling_factor)
+  }
+  standata$scaling_factor <- create_scaling_factor(standata, W)
   
-  pars <- c(if (has_intercept) "alpha", "beta", if(mod == 1) "sigma", "tau", if(family == "gaussian") "nu",
-            "mean_PPD", if (mod < 2) "psi")
+  pars <- c(if (has_intercept) "alpha", "beta", if(mod == 2) c("sigma", "tau"), if(family == "gaussian") "nu",
+            "mean_PPD", "psi")
   
   stanfit <- stanmodels$spatial
 
@@ -160,8 +209,8 @@ stan_spatial.fit <- function(x, y, w,
     } 
   }
   new_names <- c(if (has_intercept) "(Intercept)", 
-                 colnames(xtemp), "tau2", if(mod == 1) "sigma2",
-                 if(family == "gaussian") "nu2", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
+                 colnames(xtemp), if(mod == 2) c("tau", "sigma"),
+                 if(family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
   stanfit@sim$fnames_oi <- new_names
   return(structure(stanfit))  # return(structure(stanfit, prior.info = prior_info))
 }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index a63df5853..0d8757ca0 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -11,16 +11,17 @@ data {
   int edges[E_n, 2];              // adjacency pairs
   real loc_beta[K];               // priors
   real<lower=0> scale_beta[K];    // priors
-  real<lower=0> shape_tau;        // priors
-  real<lower=0> scale_tau;        // priors
-  real<lower=0> shape_sigma;      // priors
+  real<lower=0> shape1_tau;        // priors
+  real<lower=0> shape2_tau;        // priors
+  real<lower=0> loc_sigma;      // priors
   real<lower=0> scale_sigma;      // priors
-  real<lower=0> shape_nu;         // priors
+  real<lower=0> loc_nu;         // priors
   real<lower=0> scale_nu;         // priors
   real<lower=0> loc_alpha;        // priors
   real<lower=0> scale_alpha;      // priors
   int<lower=0,upper=1> has_intercept;
-  int<lower=1,upper=2> mod;
+  int<lower=1,upper=2> mod;       // 1 = icar; 2 = bym
+  real scaling_factor;
 }
 transformed data {
   real poisson_max = pow(2.0, 30.0);
@@ -28,38 +29,34 @@ transformed data {
 parameters {
   real alpha[has_intercept];  // intercept
   vector[K] beta;             // predictors on covariates (including intercept)
-  vector[N] theta_raw[mod == 1? 1 : 0];        // used for random effect (non-spatial)
+  vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0> tau;          // variance i.e. tau^2
-  real<lower=0> sigma;        // variance i.e. sigma^2
+  real<lower=0,upper=1> tau[mod == 2? 1 : 0];          // variance i.e. tau^2
+  real<lower=0> sigma[mod == 2? 1 : 0];        // variance i.e. sigma^2
   real<lower=0> nu[family == 1? 1 : 0];  // applies only if family is gaussian
 }
 transformed parameters {
-  vector[N] theta[mod == 1? 1 : 0];        // non-centered random effect (non-spatial)
   vector[N] phi;          // non-centered random effect (spatial)
-  theta[1] = sqrt(sigma) * theta_raw[1];
+  vector[N] psi;
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
-  phi = phi * sqrt(tau);
+  if (mod == 1)
+    psi = phi;
+  else if (mod == 2)
+    psi = sigma[1] *(sqrt(tau[1])*theta_raw[1] + sqrt(1-tau[1])*scaling_factor*phi);
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
   // model
   if (has_intercept == 1) {
-    if (mod == 1)
-      eta = alpha[1] + X * beta + phi + theta[1];
-    else if (mod == 2)
-      eta = alpha[1] + X * beta + phi;
+    eta = alpha[1] + X * beta + psi;
   }
   else {
-    if (mod == 1)
-      eta = X * beta + phi + theta[1];
-    else if (mod == 2)
-      eta = X * beta + phi;
+    eta = X * beta + psi;
   }
   if (family == 1) {
     target+= normal_lpdf(y_real | eta, nu[1]);
-    target+= inv_gamma_lpdf(nu[1] | shape_nu, scale_nu);
+    target+= normal_lpdf(nu[1] | loc_nu, scale_nu);
   }
   else if (family == 2) {
     target+= poisson_log_lpmf(y_int | eta);
@@ -68,38 +65,25 @@ model {
     target+= binomial_lpmf(y_int | trials, inv_logit(eta));
   }
   // priors
-  if (mod == 1)
-    target+= - N * log(sqrt(tau)) - 0.5 * inv(tau) * dot_self(phi[edges[,1]] - phi[edges[,2]]);
-  else if (mod == 2)
-    reject("leroux model not supported")
+  target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
   if (has_intercept == 1)
     target += normal_lpdf(alpha | loc_alpha, scale_alpha);
   target+= normal_lpdf(beta | loc_beta , scale_beta);
-  target+= normal_lpdf(theta_raw[1] | 0, 1);
-  target+= normal_lpdf(phi_raw | 0, 1);
-  target+= inv_gamma_lpdf(tau | shape_tau, scale_tau);
-  target+= inv_gamma_lpdf(sigma | shape_sigma, scale_sigma);
+  if (mod == 2) {
+    target+= normal_lpdf(theta_raw[1] | 0, 1);
+    target+= normal_lpdf(sigma | loc_sigma, scale_sigma);
+    target+= beta_lpdf(tau | shape1_tau, shape2_tau);
+  }
 }
 generated quantities {
   real mean_PPD = 0;
-  vector[N] psi;
-  if (mod == 1)
-    psi = theta[1] + phi;
-  else if (mod == 2)
-    psi = phi;
   {
     vector[N] eta;
     if (has_intercept == 1) {
-      if (mod == 1)
-        eta = alpha[1] + X * beta + phi + theta[1];
-      else if (mod == 2)
-        eta = alpha[1] + X * beta + phi;
+      eta = alpha[1] + X * beta + psi;
     }
     else {
-      if (mod == 1)
-        eta = X * beta + phi + theta[1];
-      else if (mod == 2)
-        eta = X * beta + phi;
+      eta = X * beta + psi;
     }
     for (n in 1:N) {
       if (family == 1)

From c2e221299e26026cfa7499bd376cf9c54e0488e3 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 13 Jul 2017 00:30:15 -0400
Subject: [PATCH 04/59] adding stan_besag (icar)

---
 DESCRIPTION                     |  1 +
 NAMESPACE                       |  1 +
 R/stan_besag.R                  | 76 +++++++++++++++++++++++++++++++++
 R/{stan_CARbym.R => stan_bym.R} |  2 +-
 R/stan_spatial.fit.R            |  8 ++--
 5 files changed, 83 insertions(+), 5 deletions(-)
 create mode 100644 R/stan_besag.R
 rename R/{stan_CARbym.R => stan_bym.R} (98%)

diff --git a/DESCRIPTION b/DESCRIPTION
index 516cb2009..a8b880b8e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -45,6 +45,7 @@ Suggests:
     digest,
     gridExtra,
     HSAUR3,
+    INLA,
     knitr (>= 1.15.1),
     MASS,
     mgcv (>= 1.8-13),
diff --git a/NAMESPACE b/NAMESPACE
index e227c0792..0f97f6b97 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -95,6 +95,7 @@ export(ranef)
 export(se)
 export(sigma)
 export(stan_aov)
+export(stan_besag)
 export(stan_betareg)
 export(stan_betareg.fit)
 export(stan_biglm)
diff --git a/R/stan_besag.R b/R/stan_besag.R
new file mode 100644
index 000000000..819595280
--- /dev/null
+++ b/R/stan_besag.R
@@ -0,0 +1,76 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2013, 2014, 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+#' Bayesian CAR Intrinsic Autoregressive models via Stan
+#'
+#' Spatial regression modeling with an intrinsic conditional autoregressive (ICAR) prior.
+#'
+#' @export
+#' 
+
+stan_besag <- function(formula,
+                     family = c("binomial", "poisson", "gaussian"),
+                     data,
+                     trials = NULL,
+                     W,
+                     ...,
+                     prior = normal(), prior_intercept = normal(),
+                     prior_nu = NULL,
+                     prior_PD = FALSE,
+                     algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                     adapt_delta = NULL,
+                     QR = FALSE) {
+  stan_function <- "stan_besag"
+  if (!requireNamespace("INLA", quietly = TRUE))
+    stop(paste("Please install the INLA package before using", stan_function))
+  mc <- match.call(expand.dots = FALSE)
+  algorithm <- match.arg(algorithm)
+  family <- match.arg(family)
+  mf <- model.frame(mc, data)
+  Y <- array1D_check(model.response(mf, type = "any"))
+  X <- model.matrix(formula, data)
+  stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
+                              trials = trials,
+                              family = family,
+                              stan_function = stan_function,
+                              ...,
+                              prior = prior,
+                              prior_intercept = prior_intercept,
+                              prior_nu = prior_nu,
+                              prior_PD = prior_PD,
+                              algorithm = algorithm, adapt_delta = adapt_delta, 
+                              QR = QR)
+  fit <- nlist(stanfit,
+               algorithm,
+               data,
+               x = X, y = Y,
+               family,
+               formula,
+               model = mf, 
+               call = match.call(),
+               stan_function = stan_function)
+  if (family == "binomial") {
+    fit$family <- binomial(link = "logit")
+    fit$trials <- trials
+  }
+  else if (family == "poisson")
+    fit$family <- poisson(link = "log")
+  else if (family == "gaussian")
+    fit$family <- gaussian(link = "identity")
+  out <- stanreg(fit)
+  structure(out, class = c("stanreg", "car"))
+}
diff --git a/R/stan_CARbym.R b/R/stan_bym.R
similarity index 98%
rename from R/stan_CARbym.R
rename to R/stan_bym.R
index 1d4316726..cd29e09ae 100644
--- a/R/stan_CARbym.R
+++ b/R/stan_bym.R
@@ -46,7 +46,7 @@ stan_bym <- function(formula,
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
                               trials = trials,
                               family = family,
-                              stan_function,
+                              stan_function = stan_function,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 79fff3ba9..4d0027f25 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -21,10 +21,10 @@
 stan_spatial.fit <- function(x, y, w,
                              trials = NULL,
                              family = c("binomial", "poisson", "gaussian"),
-                             stan_function,
+                             stan_function = c("stan_besag", "stan_bym"),
                              ...,
                              prior = normal(), prior_intercept = normal(),
-                             prior_sigma = normal(), prior_tau = beta(), prior_nu = NULL,
+                             prior_sigma = NULL, prior_tau = NULL, prior_nu = NULL,
                              prior_PD = FALSE,
                              algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                              adapt_delta = NULL,
@@ -55,7 +55,7 @@ stan_spatial.fit <- function(x, y, w,
     }
   }
   
-  if (stan_function == "stan_icar")
+  if (stan_function == "stan_besag")
     mod <- 1
   else if (stan_function == "stan_bym")
     mod <- 2
@@ -95,7 +95,7 @@ stan_spatial.fit <- function(x, y, w,
     
     prior_tau_stuff <- list(alpha = prior_tau$alpha, beta = prior_tau$beta)
   }
-  else if (stan_function == "stan_icar") {
+  else if (stan_function == "stan_besag") {
     prior_tau_stuff <- list(alpha = 0, beta = 0)
     prior_sigma_stuff <- list(prior_mean = 0, prior_scale = 0)
   }

From 5b3fda89dee18a1854f0e7cee27fb64a1ecf47b0 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 13 Jul 2017 00:48:11 -0400
Subject: [PATCH 05/59] fixing conditional in stanreg for car models

---
 R/stan_bym.R | 1 +
 R/stanreg.R  | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/stan_bym.R b/R/stan_bym.R
index cd29e09ae..ecffb7ed9 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -63,6 +63,7 @@ stan_bym <- function(formula,
                model = mf, 
                call = match.call(),
                stan_function = stan_function)
+  
   if (family == "binomial") {
     fit$family <- binomial(link = "logit")
     fit$trials <- trials
diff --git a/R/stanreg.R b/R/stanreg.R
index 2d31884b5..53f6c23fa 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -23,7 +23,7 @@
 stanreg <- function(object) {
   opt <- object$algorithm == "optimizing"
   mer <- !is.null(object$glmod) # used stan_(g)lmer
-  is_car <- object$stan_function %in% c("stan_CARbym", "stan_CARleroux")
+  is_car <- object$stan_function %in% c("stan_besag", "stan_bym")
   stanfit <- object$stanfit
   family <- object$family
   y <- object$y

From 55fb6c6df2e636e2cd6720761f60cee31a30d910 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 20 Jul 2017 22:48:44 -0400
Subject: [PATCH 06/59] expanding prior options for CAR models

---
 R/stan_besag.R       |   4 +-
 R/stan_spatial.fit.R |  84 ++++++++++++++++++++++++----------
 exec/spatial.stan    | 105 ++++++++++++++++++++++++++++++++-----------
 3 files changed, 143 insertions(+), 50 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 819595280..8889c61ce 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -28,7 +28,7 @@ stan_besag <- function(formula,
                      trials = NULL,
                      W,
                      ...,
-                     prior = normal(), prior_intercept = normal(),
+                     prior = normal(), prior_intercept = normal(), prior_tau = normal(),
                      prior_nu = NULL,
                      prior_PD = FALSE,
                      algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
@@ -43,6 +43,7 @@ stan_besag <- function(formula,
   mf <- model.frame(mc, data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
+
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
                               trials = trials,
                               family = family,
@@ -50,6 +51,7 @@ stan_besag <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
+                              prior_tau = prior_tau,
                               prior_nu = prior_nu,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 4d0027f25..a10e69ca2 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -33,12 +33,13 @@ stan_spatial.fit <- function(x, y, w,
   
   algorithm <- match.arg(algorithm)
   family <- match.arg(family)
-  
+  trials <- ifelse(is.null(trials), rep(0,length(y)), trials)
+  # Replace all this stuff with family = binomial(link = "logit") calls etc.
   if (family == "gaussian") {
     y_real <- y
     y_int <- rep(0, length(y))
     link <- "identity"
-    trials <- rep(0, length(Y))
+    trials <- rep(0, length(y))
     family_num <- 1
   }
   else {
@@ -50,7 +51,7 @@ stan_spatial.fit <- function(x, y, w,
     }
     else {  # poisson
       link <- "log"
-      trials <- rep(0, length(Y))
+      trials <- rep(0, length(y))
       family_num <- 2
     }
   }
@@ -66,10 +67,9 @@ stan_spatial.fit <- function(x, y, w,
     assign(i, x_stuff[[i]])
   nvars <- ncol(xtemp)
   
-  # temporarily suppress certain prior options
-  # ok_dists <- nlist("normal", student_t = "t", "cauchy")
-  ok_dists <- nlist("normal")
+  ok_dists <- nlist("normal", student_t = "t", "cauchy")
   ok_intercept_dists <- ok_dists
+  ok_scale_dists <- nlist("normal", student_t = "t", "cauchy", "exponential")
 
   # Deal with prior
   prior_stuff <- handle_glm_prior(prior, nvars, link, default_scale = 2.5, 
@@ -89,29 +89,50 @@ stan_spatial.fit <- function(x, y, w,
   # Deal with prior_tau and prior_sigma
   if (stan_function == "stan_bym") {
     prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, link, default_scale = 1, 
-                                          ok_dists = "normal")
+                                          ok_dists = ok_scale_dists)
+    names(prior_sigma_stuff) <- paste0(names(prior_sigma_stuff), 
+                                           "_for_aux")
     for (i in names(prior_sigma_stuff)) # prior_{dist, mean, scale, df, autoscale}
       assign(i, prior_sigma_stuff[[i]])
     
     prior_tau_stuff <- list(alpha = prior_tau$alpha, beta = prior_tau$beta)
+    prior_dist_for_tau <- 0
+    prior_mean_for_tau <- 0
+    prior_scale_for_tau <- 1
+    prior_df_for_tau <- 1
   }
   else if (stan_function == "stan_besag") {
-    prior_tau_stuff <- list(alpha = 0, beta = 0)
-    prior_sigma_stuff <- list(prior_mean = 0, prior_scale = 0)
+    prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, link, default_scale = 1, 
+                                          ok_dists = ok_scale_dists)
+    names(prior_tau_stuff) <- paste0(names(prior_tau_stuff), 
+                                       "_for_tau")
+    for (i in names(prior_tau_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_tau_stuff[[i]])
+    prior_tau_stuff$alpha <- 0
+    prior_tau_stuff$beta <- 0
+    prior_dist_for_aux <- 0
+    prior_mean_for_aux <- 0
+    prior_scale_for_aux <- 1
+    prior_df_for_aux <- 1
   }
-  
-  # Deal with prior_nu
+
+  # Deal with prior_nu... needs more dist options
   if (family == "gaussian") {
     prior_nu_stuff <- handle_glm_prior(prior_nu, nvars = 1, link, default_scale = 1, 
-                                    ok_dists = "normal")
+                                    ok_dists = ok_dists)
+    names(prior_nu_stuff) <- paste0(names(prior_nu_stuff), 
+                                     "_for_nu")
     for (i in names(prior_nu_stuff)) # prior_{dist, mean, scale, df, autoscale}
       assign(i, prior_nu_stuff[[i]])
   }
   else {
-    prior_nu_stuff <- list(prior_mean = 0, prior_scale = 0)
+    prior_dist_for_nu <- 0
+    prior_mean_for_nu <- 0
+    prior_scale_for_nu <- 1
+    prior_df_for_nu <- 1
   }
   
-  # QR decomposition for both x and z
+  # QR decomposition for x
   if (QR) {
     if (nvars <= 1)
       stop("'QR' can only be specified when there are multiple predictors.")
@@ -141,20 +162,35 @@ stan_spatial.fit <- function(x, y, w,
                     edges = edges,
                     E_n = nrow(edges),
                     family = family_num,
+                    link = 0,  # FIX ME!!!
                     X = if (has_intercept) x[,-1] else x ,  # use xtemp
                     y_real = y_real,
                     y_int = y_int,
                     trials = trials,
                     shape1_tau = c(prior_tau_stuff$alpha),
                     shape2_tau = c(prior_tau_stuff$beta),
-                    loc_sigma = c(prior_sigma_stuff$prior_mean),
-                    scale_sigma = c(prior_sigma_stuff$prior_scale),
-                    loc_nu = c(prior_nu_stuff$prior_mean),
-                    scale_nu = c(prior_nu_stuff$prior_scale),
-                    loc_beta = as.array(prior_stuff$prior_mean),
-                    scale_beta = as.array(prior_stuff$prior_scale),
-                    loc_alpha = c(prior_intercept_stuff$prior_mean_for_intercept),
-                    scale_alpha = c(prior_intercept_stuff$prior_scale_for_intercept),
+                    prior_dist_for_intercept = prior_dist_for_intercept,
+                    prior_dist = prior_dist,
+                    prior_dist_tau = prior_dist_for_tau,
+                    prior_dist_aux = prior_dist_for_aux,
+                    prior_dist_nu = prior_dist_for_nu,
+                    prior_mean_for_intercept = c(prior_mean_for_intercept),
+                    prior_scale_for_intercept = c(prior_scale_for_intercept),
+                    prior_df_for_intercept = c(prior_df_for_intercept),
+                    prior_mean = as.array(prior_mean),
+                    prior_scale = as.array(prior_scale),
+                    prior_df = c(prior_df),
+                    prior_mean_aux = c(prior_mean_for_aux),
+                    prior_scale_aux = c(prior_scale_for_aux),
+                    prior_df_aux = c(prior_df_for_aux),
+                    prior_rate_aux = 0,  # FIX ME!!!
+                    prior_mean_tau = c(prior_mean_for_tau),
+                    prior_scale_tau = c(prior_scale_for_tau),
+                    prior_df_tau = c(prior_df_for_tau),
+                    prior_rate_tau = 0,  # FIX ME!!!
+                    prior_mean_nu = c(prior_mean_for_nu),
+                    prior_scale_nu = c(prior_scale_for_nu),
+                    prior_df_nu = c(prior_df_for_nu),
                     has_intercept = has_intercept,
                     mod = mod)
   standata$X <- array(standata$X, dim = c(standata$N, standata$K))
@@ -177,7 +213,7 @@ stan_spatial.fit <- function(x, y, w,
   }
   standata$scaling_factor <- create_scaling_factor(standata, W)
   
-  pars <- c(if (has_intercept) "alpha", "beta", if(mod == 2) c("sigma", "tau"), if(family == "gaussian") "nu",
+  pars <- c(if (has_intercept) "alpha", "beta", "tau", if(mod == 2) c("sigma"), if(family == "gaussian") "nu",
             "mean_PPD", "psi")
   
   stanfit <- stanmodels$spatial
@@ -209,7 +245,7 @@ stan_spatial.fit <- function(x, y, w,
     } 
   }
   new_names <- c(if (has_intercept) "(Intercept)", 
-                 colnames(xtemp), if(mod == 2) c("tau", "sigma"),
+                 colnames(xtemp), "tau", if(mod == 2) c("sigma"),
                  if(family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
   stanfit@sim$fnames_oi <- new_names
   return(structure(stanfit))  # return(structure(stanfit, prior.info = prior_info))
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 0d8757ca0..30234612d 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -1,4 +1,4 @@
-// CAR (BYM version i.e. IAR)
+// CAR SPATIAL MODELS
 data {
   int<lower=0> N;                 // number of regions
   int<lower=0> K;                 // number of predictors (inc intercept)
@@ -7,31 +7,46 @@ data {
   int y_int[N];                   // outcome
   real y_real[N];                 // outcome
   int<lower=1,upper=3> family;    // family (1 = Gaussian, 2 = Poisson, 3 = Binomial)
+  int link;
   int E_n;                        // number of adjacency pairs
   int edges[E_n, 2];              // adjacency pairs
-  real loc_beta[K];               // priors
-  real<lower=0> scale_beta[K];    // priors
   real<lower=0> shape1_tau;        // priors
   real<lower=0> shape2_tau;        // priors
-  real<lower=0> loc_sigma;      // priors
-  real<lower=0> scale_sigma;      // priors
-  real<lower=0> loc_nu;         // priors
-  real<lower=0> scale_nu;         // priors
-  real<lower=0> loc_alpha;        // priors
-  real<lower=0> scale_alpha;      // priors
   int<lower=0,upper=1> has_intercept;
-  int<lower=1,upper=2> mod;       // 1 = icar; 2 = bym
+  int<lower=1,upper=2> mod;       // 1 = besag (icar); 2 = bym
   real scaling_factor;
+  int<lower=0> prior_dist_for_intercept;
+  int<lower=0> prior_dist;
+  int<lower=0> prior_dist_aux;
+  int<lower=0> prior_dist_tau;
+  int<lower=0> prior_dist_nu;
+  real prior_mean_for_intercept;
+  real<lower=0> prior_scale_for_intercept;
+  real<lower=0> prior_df_for_intercept;
+  vector[K] prior_mean;
+  vector<lower=0>[K] prior_scale;
+  real<lower=0> prior_df;
+  real prior_mean_aux;
+  real<lower=0> prior_scale_aux;
+  real<lower=0> prior_df_aux;
+  real<lower=0> prior_rate_aux;
+  real prior_mean_tau;
+  real<lower=0> prior_scale_tau;
+  real<lower=0> prior_df_tau;
+  real<lower=0> prior_rate_tau;
+  real prior_mean_nu;
+  real<lower=0> prior_scale_nu;
+  real<lower=0> prior_df_nu;
 }
 transformed data {
-  real poisson_max = pow(2.0, 30.0);
+  real poisson_max = 30 * log(2);
 }
 parameters {
   real alpha[has_intercept];  // intercept
   vector[K] beta;             // predictors on covariates (including intercept)
   vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0,upper=1> tau[mod == 2? 1 : 0];          // variance i.e. tau^2
+  real<lower=0,upper=(mod == 2? 1: positive_infinity())> tau;          // variance i.e. tau^2
   real<lower=0> sigma[mod == 2? 1 : 0];        // variance i.e. sigma^2
   real<lower=0> nu[family == 1? 1 : 0];  // applies only if family is gaussian
 }
@@ -41,22 +56,20 @@ transformed parameters {
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
   if (mod == 1)
-    psi = phi;
+    psi = phi * sqrt(inv(tau));
   else if (mod == 2)
-    psi = sigma[1] *(sqrt(tau[1])*theta_raw[1] + sqrt(1-tau[1])*scaling_factor*phi);
+    psi = sigma[1]*(sqrt(1-tau)*theta_raw[1] + sqrt(tau/scaling_factor)*phi);
+    // psi = sigma[1]*(sqrt(tau)*theta_raw[1] + sqrt(1-tau)*scaling_factor*phi);
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
   // model
-  if (has_intercept == 1) {
+  if (has_intercept == 1)
     eta = alpha[1] + X * beta + psi;
-  }
-  else {
+  else
     eta = X * beta + psi;
-  }
   if (family == 1) {
     target+= normal_lpdf(y_real | eta, nu[1]);
-    target+= normal_lpdf(nu[1] | loc_nu, scale_nu);
   }
   else if (family == 2) {
     target+= poisson_log_lpmf(y_int | eta);
@@ -64,15 +77,57 @@ model {
   else if (family == 3) {
     target+= binomial_lpmf(y_int | trials, inv_logit(eta));
   }
-  // priors
+  // prior on spatial parameter
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
-  if (has_intercept == 1)
-    target += normal_lpdf(alpha | loc_alpha, scale_alpha);
-  target+= normal_lpdf(beta | loc_beta , scale_beta);
-  if (mod == 2) {
+  // priors on coefficients
+  if (has_intercept == 1) {
+    if (prior_dist_for_intercept == 1)
+      target+= normal_lpdf(alpha | prior_mean_for_intercept, prior_scale_for_intercept);
+    else if (prior_dist_for_intercept == 2)
+      target+= student_t_lpdf(alpha | prior_df_for_intercept, prior_mean_for_intercept, prior_scale_for_intercept);
+    /* else prior_dist_intercept is 0 and nothing is added */
+  }
+  if (K > 0) {
+    if (prior_dist == 1)
+      target+= normal_lpdf(beta | prior_mean, prior_scale);
+    else if (prior_dist == 2)
+      target+= student_t_lpdf(beta | prior_df, prior_mean, prior_scale);
+    else if (prior_dist == 3)
+      target+= cauchy_lpdf(beta | prior_mean, prior_scale);
+    /* else prior_dist is 0 and nothing is added */
+  }
+  if (mod == 2) { // BYM
     target+= normal_lpdf(theta_raw[1] | 0, 1);
-    target+= normal_lpdf(sigma | loc_sigma, scale_sigma);
     target+= beta_lpdf(tau | shape1_tau, shape2_tau);
+    if (prior_dist_aux == 1)
+      target+= normal_lpdf(sigma | prior_mean_aux, prior_scale_aux);
+    else if (prior_dist_aux == 2)
+      target+= student_t_lpdf(sigma | prior_df_aux, prior_mean_aux, prior_scale_aux);
+    else if (prior_dist_aux == 3)
+      target+= cauchy_lpdf(sigma | prior_mean_aux, prior_scale_aux);
+    else if (prior_dist_aux == 4)
+      target+= exponential_lpdf(sigma | prior_rate_aux);
+    /* else prior_dist_aux is 0 and nothing is added */
+  }
+  else {
+    if (prior_dist_tau == 1)
+      target+= normal_lpdf(tau | prior_mean_tau, prior_scale_tau);
+    else if (prior_dist_tau == 2)
+      target+= student_t_lpdf(tau | prior_df_tau, prior_mean_tau, prior_scale_tau);
+    else if (prior_dist_tau == 3)
+      target+= cauchy_lpdf(tau | prior_mean_tau, prior_scale_tau);
+    else if (prior_dist_tau == 4)
+      target+= exponential_lpdf(tau | prior_rate_tau);
+    /* else prior_dist_tau is 0 and nothing is added */
+  }
+  if (family == 1) { // prior on sd if outcome is gaussian
+    if (prior_dist_nu == 1)
+      target+= normal_lpdf(nu[1] | prior_mean_nu, prior_scale_nu);
+    else if (prior_dist_nu == 2)
+      target+= student_t_lpdf(nu[1] | prior_df_nu, prior_mean_nu, prior_scale_nu);
+    else if (prior_dist_nu == 3)
+      target+= cauchy_lpdf(nu[1] | prior_mean_nu, prior_scale_nu);
+    /* else prior_dist_nu is 0 and nothing is added */
   }
 }
 generated quantities {

From 6247da9c32c841dad1532ff9a441296017dcca08 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 24 Jul 2017 23:49:25 -0400
Subject: [PATCH 07/59] changes to spatial.fit file

---
 R/stan_spatial.fit.R | 102 +++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 99 insertions(+), 3 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index a10e69ca2..c42479154 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -33,7 +33,8 @@ stan_spatial.fit <- function(x, y, w,
   
   algorithm <- match.arg(algorithm)
   family <- match.arg(family)
-  trials <- ifelse(is.null(trials), rep(0,length(y)), trials)
+  if (is.null(trials))
+    trials <- rep(0,length(y))
   # Replace all this stuff with family = binomial(link = "logit") calls etc.
   if (family == "gaussian") {
     y_real <- y
@@ -215,7 +216,21 @@ stan_spatial.fit <- function(x, y, w,
   
   pars <- c(if (has_intercept) "alpha", "beta", "tau", if(mod == 2) c("sigma"), if(family == "gaussian") "nu",
             "mean_PPD", "psi")
-  
+
+  prior_info <- summarize_spatial_prior(
+    user_prior = prior_stuff,
+    user_prior_intercept = prior_intercept_stuff,
+    user_prior_tau = prior_tau_stuff,
+    user_prior_nu = prior_nu_stuff,
+    user_prior_aux = prior_aux_stuff,
+    has_intercept = has_intercept,
+    has_predictors = nvars > 0,
+    adjusted_prior_scale = prior_scale,
+    adjusted_prior_intercept_scale = prior_scale_for_intercept,
+    adjusted_prior_scale_tau = prior_scale_for_tau,
+    adjusted_prior_scale_nu = prior_scale_for_nu,
+    adjusted_prior_scale_aux = prior_scale_for_aux)
+  browser()
   stanfit <- stanmodels$spatial
 
   if (algorithm == "sampling") {
@@ -248,5 +263,86 @@ stan_spatial.fit <- function(x, y, w,
                  colnames(xtemp), "tau", if(mod == 2) c("sigma"),
                  if(family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
   stanfit@sim$fnames_oi <- new_names
-  return(structure(stanfit))  # return(structure(stanfit, prior.info = prior_info))
+  return(structure(stanfit, prior.info = prior_info))
+}
+
+# Summarize spatial prior
+
+summarize_spatial_prior <- function(user_prior,
+                                    user_prior_intercept,
+                                    user_prior_tau,
+                                    user_prior_nu,
+                                    user_prior_aux,
+                                    has_intercept,
+                                    has_predictors,
+                                    adjusted_prior_scale,
+                                    adjusted_prior_intercept_scale,
+                                    adjusted_prior_scale_tau,
+                                    adjusted_prior_scale_nu,
+                                    adjusted_prior_scale_aux) {
+  rescaled_coef <-
+    user_prior$prior_autoscale && has_predictors &&
+    !is.na(user_prior$prior_dist_name) &&
+    !all(user_prior$prior_scale == adjusted_prior_scale)
+  rescaled_int <-
+    user_prior_intercept$prior_autoscale_for_intercept && has_intercept &&
+    !is.na(user_prior_intercept$prior_dist_name_for_intercept) &&
+    (user_prior_intercept$prior_scale != adjusted_prior_intercept_scale)
+  
+  if (has_predictors && user_prior$prior_dist_name %in% "t") {
+    if (all(user_prior$prior_df == 1)) {
+      user_prior$prior_dist_name <- "cauchy"
+    } else {
+      user_prior$prior_dist_name <- "student_t"
+    }
+  }
+  if (has_intercept &&
+      user_prior_intercept$prior_dist_name_for_intercept %in% "t") {
+    if (all(user_prior_intercept$prior_df_for_intercept == 1)) {
+      user_prior_intercept$prior_dist_name_for_intercept <- "cauchy"
+    } else {
+      user_prior_intercept$prior_dist_name_for_intercept <- "student_t"
+    }
+  }
+  prior_list <- list(
+    prior = 
+      if (!has_predictors) NULL else with(user_prior, list(
+        dist = prior_dist_name,
+        location = prior_mean,
+        scale = prior_scale,
+        adjusted_scale = if (rescaled_coef)
+          adjusted_prior_scale else NULL,
+        df = if (prior_dist_name %in% c("student_t", "hs", "hs_plus", 
+                                        "lasso", "product_normal"))
+          prior_df else NULL
+      )),
+    prior_intercept = 
+      if (!has_intercept) NULL else with(user_prior_intercept, list(
+        dist = prior_dist_name_for_intercept,
+        location = prior_mean_for_intercept,
+        scale = prior_scale_for_intercept,
+        adjusted_scale = if (rescaled_int)
+          adjusted_prior_intercept_scale else NULL,
+        df = if (prior_dist_name_for_intercept %in% "student_t")
+          prior_df_for_intercept else NULL
+      ))#,
+    #prior_aux = 
+    #  if (!has_phi) NULL else with(user_prior_aux, list(
+    #    dist = prior_dist_name_for_aux,
+    #    location = if (!is.na(prior_dist_name_for_aux) && 
+    #                   prior_dist_name_for_aux != "exponential")
+    #      prior_mean_for_aux else NULL,
+    #    scale = if (!is.na(prior_dist_name_for_aux) && 
+    #                prior_dist_name_for_aux != "exponential")
+    #      prior_scale_for_aux else NULL,
+    #    df = if (!is.na(prior_dist_name_for_aux) && 
+    #             prior_dist_name_for_aux %in% "student_t")
+    #      prior_df_for_aux else NULL, 
+    #    rate = if (!is.na(prior_dist_name_for_aux) && 
+    #               prior_dist_name_for_aux %in% "exponential")
+    #      1 / prior_scale_for_aux else NULL,
+    #    aux_name = "phi"
+    #  ))
+  )
+  return(prior_list)
 }

From e3ea475aca70d9b1c8b580ab89194c079d648284 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Fri, 4 Aug 2017 16:51:03 -0400
Subject: [PATCH 08/59] adding linkinv chunks to spatial.stan

---
 NAMESPACE            |  5 +----
 R/stan_besag.R       | 13 +++++--------
 R/stan_spatial.fit.R | 45 ++++++++++++++++++++++++++------------------
 exec/spatial.stan    | 25 ++++++++++++++++++------
 4 files changed, 52 insertions(+), 36 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index d710962cf..822a103a2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -55,11 +55,8 @@ S3method(vcov,stanreg)
 S3method(waic,stanreg)
 export(R2)
 export(VarCorr)
-<<<<<<< HEAD
-export(beta)
-=======
 export(bayes_R2)
->>>>>>> master
+export(beta)
 export(cauchy)
 export(compare_models)
 export(decov)
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 8889c61ce..07f70228f 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -23,7 +23,7 @@
 #' 
 
 stan_besag <- function(formula,
-                     family = c("binomial", "poisson", "gaussian"),
+                     family = gaussian(),
                      data,
                      trials = NULL,
                      W,
@@ -39,11 +39,11 @@ stan_besag <- function(formula,
     stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
-  family <- match.arg(family)
+  family <- validate_family(family)
   mf <- model.frame(mc, data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
-
+  
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
                               trials = trials,
                               family = family,
@@ -65,14 +65,11 @@ stan_besag <- function(formula,
                model = mf, 
                call = match.call(),
                stan_function = stan_function)
-  if (family == "binomial") {
+ 
+  if (family$family == "binomial") {
     fit$family <- binomial(link = "logit")
     fit$trials <- trials
   }
-  else if (family == "poisson")
-    fit$family <- poisson(link = "log")
-  else if (family == "gaussian")
-    fit$family <- gaussian(link = "identity")
   out <- stanreg(fit)
   structure(out, class = c("stanreg", "car"))
 }
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index c42479154..0cc22b6ee 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -20,7 +20,7 @@
 
 stan_spatial.fit <- function(x, y, w,
                              trials = NULL,
-                             family = c("binomial", "poisson", "gaussian"),
+                             family = gaussian(),
                              stan_function = c("stan_besag", "stan_bym"),
                              ...,
                              prior = normal(), prior_intercept = normal(),
@@ -32,26 +32,34 @@ stan_spatial.fit <- function(x, y, w,
   # check that W is appropriate
   
   algorithm <- match.arg(algorithm)
-  family <- match.arg(family)
+
+  family <- validate_family(family)
+  supported_families <- c("binomial", "gaussian", "poisson")
+  fam <- which(pmatch(supported_families, family$family, nomatch = 0L) == 1L)
+  if (!length(fam)) 
+    stop("'family' must be one of ", paste(supported_families, collapse = ", "))
+  
+  supported_links <- supported_glm_links(supported_families[fam])
+  link <- which(supported_links == family$link)
+  if (!length(link)) 
+    stop("'link' must be one of ", paste(supported_links, collapse = ", "))
+  
   if (is.null(trials))
     trials <- rep(0,length(y))
   # Replace all this stuff with family = binomial(link = "logit") calls etc.
-  if (family == "gaussian") {
+  if (family$family == "gaussian") {
     y_real <- y
     y_int <- rep(0, length(y))
-    link <- "identity"
     trials <- rep(0, length(y))
     family_num <- 1
   }
   else {
     y_real <- rep(0, length(y))
     y_int <- y
-    if (family == "binomial") {
-      link <- "logit"
+    if (family$family == "binomial") {
       family_num <- 3
     }
     else {  # poisson
-      link <- "log"
       trials <- rep(0, length(y))
       family_num <- 2
     }
@@ -73,14 +81,14 @@ stan_spatial.fit <- function(x, y, w,
   ok_scale_dists <- nlist("normal", student_t = "t", "cauchy", "exponential")
 
   # Deal with prior
-  prior_stuff <- handle_glm_prior(prior, nvars, link, default_scale = 2.5, 
+  prior_stuff <- handle_glm_prior(prior, nvars, family$link, default_scale = 2.5, 
                                   ok_dists = ok_dists)
   for (i in names(prior_stuff)) # prior_{dist, mean, scale, df, autoscale}
     assign(i, prior_stuff[[i]])
   
   # Deal with prior_intercept
   prior_intercept_stuff <- handle_glm_prior(prior_intercept, nvars = 1, 
-                                            default_scale = 10, link = link,
+                                            default_scale = 10, link = family$link,
                                             ok_dists = ok_intercept_dists)
   names(prior_intercept_stuff) <- paste0(names(prior_intercept_stuff), 
                                          "_for_intercept")
@@ -89,7 +97,7 @@ stan_spatial.fit <- function(x, y, w,
   
   # Deal with prior_tau and prior_sigma
   if (stan_function == "stan_bym") {
-    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, link, default_scale = 1, 
+    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, family$link, default_scale = 1, 
                                           ok_dists = ok_scale_dists)
     names(prior_sigma_stuff) <- paste0(names(prior_sigma_stuff), 
                                            "_for_aux")
@@ -103,7 +111,7 @@ stan_spatial.fit <- function(x, y, w,
     prior_df_for_tau <- 1
   }
   else if (stan_function == "stan_besag") {
-    prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, link, default_scale = 1, 
+    prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, family$link, default_scale = 1, 
                                           ok_dists = ok_scale_dists)
     names(prior_tau_stuff) <- paste0(names(prior_tau_stuff), 
                                        "_for_tau")
@@ -118,8 +126,8 @@ stan_spatial.fit <- function(x, y, w,
   }
 
   # Deal with prior_nu... needs more dist options
-  if (family == "gaussian") {
-    prior_nu_stuff <- handle_glm_prior(prior_nu, nvars = 1, link, default_scale = 1, 
+  if (family$family == "gaussian") {
+    prior_nu_stuff <- handle_glm_prior(prior_nu, nvars = 1, family$link, default_scale = 1, 
                                     ok_dists = ok_dists)
     names(prior_nu_stuff) <- paste0(names(prior_nu_stuff), 
                                      "_for_nu")
@@ -163,8 +171,9 @@ stan_spatial.fit <- function(x, y, w,
                     edges = edges,
                     E_n = nrow(edges),
                     family = family_num,
-                    link = 0,  # FIX ME!!!
-                    X = if (has_intercept) x[,-1] else x ,  # use xtemp
+                    link = link,
+                    X = if (has_intercept) x[,-1] else x ,  # include decorrelation and use xtemp
+                    xbar = as.array(xbar),
                     y_real = y_real,
                     y_int = y_int,
                     trials = trials,
@@ -214,7 +223,7 @@ stan_spatial.fit <- function(x, y, w,
   }
   standata$scaling_factor <- create_scaling_factor(standata, W)
   
-  pars <- c(if (has_intercept) "alpha", "beta", "tau", if(mod == 2) c("sigma"), if(family == "gaussian") "nu",
+  pars <- c(if (has_intercept) "alpha", "beta", "tau", if(mod == 2) c("sigma"), if(family$family == "gaussian") "nu",
             "mean_PPD", "psi")
 
   prior_info <- summarize_spatial_prior(
@@ -230,7 +239,7 @@ stan_spatial.fit <- function(x, y, w,
     adjusted_prior_scale_tau = prior_scale_for_tau,
     adjusted_prior_scale_nu = prior_scale_for_nu,
     adjusted_prior_scale_aux = prior_scale_for_aux)
-  browser()
+
   stanfit <- stanmodels$spatial
 
   if (algorithm == "sampling") {
@@ -261,7 +270,7 @@ stan_spatial.fit <- function(x, y, w,
   }
   new_names <- c(if (has_intercept) "(Intercept)", 
                  colnames(xtemp), "tau", if(mod == 2) c("sigma"),
-                 if(family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
+                 if(family$family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
   stanfit@sim$fnames_oi <- new_names
   return(structure(stanfit, prior.info = prior_info))
 }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 30234612d..e38e0c09e 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -1,8 +1,14 @@
 // CAR SPATIAL MODELS
+functions {
+  #include "continuous_likelihoods.stan"
+  #include "binomial_likelihoods.stan"
+  #include "count_likelihoods.stan"
+}
 data {
   int<lower=0> N;                 // number of regions
   int<lower=0> K;                 // number of predictors (inc intercept)
   matrix[N,K] X;                  // model matrix
+  vector[K] xbar;
   int<lower=0> trials[N];         // binomial trials (0 1d array if not applicable)
   int y_int[N];                   // outcome
   real y_real[N];                 // outcome
@@ -42,7 +48,7 @@ transformed data {
   real poisson_max = 30 * log(2);
 }
 parameters {
-  real alpha[has_intercept];  // intercept
+  real gamma[has_intercept];  // raw intercept
   vector[K] beta;             // predictors on covariates (including intercept)
   vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
@@ -63,28 +69,32 @@ transformed parameters {
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
-  // model
+  // deal with intercept
   if (has_intercept == 1)
-    eta = alpha[1] + X * beta + psi;
+    eta = gamma[1] + X * beta + psi;
   else
     eta = X * beta + psi;
+  // likelihoods
   if (family == 1) {
+    eta = linkinv_gauss(eta, link);
     target+= normal_lpdf(y_real | eta, nu[1]);
   }
   else if (family == 2) {
+    eta = linkinv_count(eta, link);
     target+= poisson_log_lpmf(y_int | eta);
   }
   else if (family == 3) {
+    eta = linkinv_binom(eta, link);
     target+= binomial_lpmf(y_int | trials, inv_logit(eta));
   }
-  // prior on spatial parameter
+  // prior on spatial parameter vector
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
   // priors on coefficients
   if (has_intercept == 1) {
     if (prior_dist_for_intercept == 1)
-      target+= normal_lpdf(alpha | prior_mean_for_intercept, prior_scale_for_intercept);
+      target+= normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
     else if (prior_dist_for_intercept == 2)
-      target+= student_t_lpdf(alpha | prior_df_for_intercept, prior_mean_for_intercept, prior_scale_for_intercept);
+      target+= student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept, prior_scale_for_intercept);
     /* else prior_dist_intercept is 0 and nothing is added */
   }
   if (K > 0) {
@@ -132,6 +142,9 @@ model {
 }
 generated quantities {
   real mean_PPD = 0;
+  real alpha[has_intercept];
+  if (has_intercept)
+    alpha[1] = gamma[1] - dot_product(beta, xbar);
   {
     vector[N] eta;
     if (has_intercept == 1) {

From 4c0f42dde5cb4575586bf87dd482e93ddb942e03 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Fri, 4 Aug 2017 17:15:14 -0400
Subject: [PATCH 09/59] using chunk functions for meanPPD

---
 exec/spatial.stan | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/exec/spatial.stan b/exec/spatial.stan
index e38e0c09e..fc04c4317 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -153,17 +153,22 @@ generated quantities {
     else {
       eta = X * beta + psi;
     }
-    for (n in 1:N) {
-      if (family == 1)
-        mean_PPD = mean_PPD + normal_rng(eta[n], nu[1]);
-      else if (family == 2) {
+    if (family == 1) {
+      eta = linkinv_gauss(eta, link);
+      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], nu[1]);
+    }
+    else if (family == 2) {
+      eta = linkinv_count(eta, link);
+      for (n in 1:N) {
         if (eta[n] < poisson_max)
           mean_PPD = mean_PPD + poisson_log_rng(eta[n]);
         else
           mean_PPD = mean_PPD + normal_rng(eta[n], sqrt(eta[n]));
       }
-      else if (family == 3)
-        mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
+    }
+    else if (family == 3) {
+      eta = linkinv_binom(eta, link);
+      for (n in 1:N) mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
     }
   }
   mean_PPD = mean_PPD / N;

From 2070bb4ea21d765fab936cd215f19c62d1b35ecc Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 6 Aug 2017 17:13:50 -0400
Subject: [PATCH 10/59] demeaning, QR decom, and posterior methods work

---
 R/posterior_predict.R |  2 +-
 R/stan_besag.R        |  2 +-
 R/stan_bym.R          | 10 +++-------
 R/stan_spatial.fit.R  | 13 +++++++++----
 R/stanreg.R           |  5 +----
 exec/spatial.stan     | 42 +++++++++++++++++++++---------------------
 6 files changed, 36 insertions(+), 38 deletions(-)

diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index 3e9e43aff..7adb6df2d 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -167,7 +167,7 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
   } else {
     ppargs <- pp_args(object, data = pp_eta(object, dat, draws))
   }
-  if (!is_polr(object) && is.binomial(family(object)$family))
+  if (!is_polr(object) && is.binomial(family(object)$family))  # Simplify this
     ppargs$trials <- pp_binomial_trials(object, newdata)
   if (is(object, "car") && is.binomial(family(object)$family))
     ppargs$trials <- object$trials
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 07f70228f..4c6e072c7 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -36,7 +36,7 @@ stan_besag <- function(formula,
                      QR = FALSE) {
   stan_function <- "stan_besag"
   if (!requireNamespace("INLA", quietly = TRUE))
-    stop(paste("Please install the INLA package before using", stan_function))
+    stop(paste("Please install and load the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index ecffb7ed9..aadd987d5 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -23,7 +23,7 @@
 #' 
 
 stan_bym <- function(formula,
-                        family = c("binomial", "poisson", "gaussian"),
+                        family = gaussian(),
                         data,
                         trials = NULL,
                         W,
@@ -39,7 +39,7 @@ stan_bym <- function(formula,
     stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
-  family <- match.arg(family)
+  family <- validate_family(family)
   mf <- model.frame(mc, data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
@@ -64,14 +64,10 @@ stan_bym <- function(formula,
                call = match.call(),
                stan_function = stan_function)
   
-  if (family == "binomial") {
+  if (family$family == "binomial") {
     fit$family <- binomial(link = "logit")
     fit$trials <- trials
   }
-  else if (family == "poisson")
-    fit$family <- poisson(link = "log")
-  else if (family == "gaussian")
-    fit$family <- gaussian(link = "identity")
   out <- stanreg(fit)
   structure(out, class = c("stanreg", "car"))
 }
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 0cc22b6ee..5bfb9f85a 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -58,11 +58,16 @@ stan_spatial.fit <- function(x, y, w,
     y_int <- y
     if (family$family == "binomial") {
       family_num <- 3
+      if (is.null(trials) | any(y > trials))
+        stop("Outcome values must be less than or equal to the corresponding value in `trials`.")
+        
     }
     else {  # poisson
       trials <- rep(0, length(y))
       family_num <- 2
     }
+    if(!is.integer(y_int))
+      stop("Outcome must an integer for count likelihoods.")
   }
   
   if (stan_function == "stan_besag")
@@ -172,7 +177,7 @@ stan_spatial.fit <- function(x, y, w,
                     E_n = nrow(edges),
                     family = family_num,
                     link = link,
-                    X = if (has_intercept) x[,-1] else x ,  # include decorrelation and use xtemp
+                    X = xtemp,
                     xbar = as.array(xbar),
                     y_real = y_real,
                     y_int = y_int,
@@ -223,7 +228,7 @@ stan_spatial.fit <- function(x, y, w,
   }
   standata$scaling_factor <- create_scaling_factor(standata, W)
   
-  pars <- c(if (has_intercept) "alpha", "beta", "tau", if(mod == 2) c("sigma"), if(family$family == "gaussian") "nu",
+  pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "sigma",
             "mean_PPD", "psi")
 
   prior_info <- summarize_spatial_prior(
@@ -269,8 +274,8 @@ stan_spatial.fit <- function(x, y, w,
     } 
   }
   new_names <- c(if (has_intercept) "(Intercept)", 
-                 colnames(xtemp), "tau", if(mod == 2) c("sigma"),
-                 if(family$family == "gaussian") "nu", "mean_PPD", "log-posterior", paste0("psi[", 1:standata$N, "]"))
+                 colnames(xtemp), "rho", if(mod == 2) c("tau"),
+                 if(family$family == "gaussian") "sigma", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
   stanfit@sim$fnames_oi <- new_names
   return(structure(stanfit, prior.info = prior_info))
 }
diff --git a/R/stanreg.R b/R/stanreg.R
index 53f6c23fa..1889605b2 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -23,7 +23,6 @@
 stanreg <- function(object) {
   opt <- object$algorithm == "optimizing"
   mer <- !is.null(object$glmod) # used stan_(g)lmer
-  is_car <- object$stan_function %in% c("stan_besag", "stan_bym")
   stanfit <- object$stanfit
   family <- object$family
   y <- object$y
@@ -32,6 +31,7 @@ stanreg <- function(object) {
   nobs <- NROW(y)
   ynames <- if (is.matrix(y)) rownames(y) else names(y)
   
+  is_car <- object$stan_function %in% c("stan_besag", "stan_bym")
   is_betareg <- is.beta(family$family)
   if (is_betareg) { 
     family_phi <- object$family_phi  # pull out phi family/link
@@ -156,9 +156,6 @@ stanreg <- function(object) {
     out$phi <- phi
   }
   if (is_car) {
-    # CARBayes doesn't include the scale parameters in out$coefficients so not sure if we want to.
-    # exclude <- c(1:nvars, psi_indx, which(rownames(stan_summary) %in% c("mean_PPD", "log-posterior")))
-    # out$coefficients = c(coefs, stan_summary[-exclude, select_median(object$algorithm)])
     out$psi <- psi
     out$trials <- object$trials
   }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index fc04c4317..086899d2c 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -31,7 +31,7 @@ data {
   real<lower=0> prior_df_for_intercept;
   vector[K] prior_mean;
   vector<lower=0>[K] prior_scale;
-  real<lower=0> prior_df;
+  real<lower=0> prior_df[K];
   real prior_mean_aux;
   real<lower=0> prior_scale_aux;
   real<lower=0> prior_df_aux;
@@ -52,9 +52,9 @@ parameters {
   vector[K] beta;             // predictors on covariates (including intercept)
   vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0,upper=(mod == 2? 1: positive_infinity())> tau;          // variance i.e. tau^2
-  real<lower=0> sigma[mod == 2? 1 : 0];        // variance i.e. sigma^2
-  real<lower=0> nu[family == 1? 1 : 0];  // applies only if family is gaussian
+  real<lower=0,upper=(mod == 2? 1: positive_infinity())> rho;          // variance i.e. rho^2
+  real<lower=0> tau[mod == 2? 1 : 0];        // variance i.e. tau^2
+  real<lower=0> sigma[family == 1? 1 : 0];  // applies only if family is gaussian
 }
 transformed parameters {
   vector[N] phi;          // non-centered random effect (spatial)
@@ -62,10 +62,10 @@ transformed parameters {
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
   if (mod == 1)
-    psi = phi * sqrt(inv(tau));
+    psi = phi * sqrt(inv(rho));
   else if (mod == 2)
-    psi = sigma[1]*(sqrt(1-tau)*theta_raw[1] + sqrt(tau/scaling_factor)*phi);
-    // psi = sigma[1]*(sqrt(tau)*theta_raw[1] + sqrt(1-tau)*scaling_factor*phi);
+    psi = tau[1]*(sqrt(1-rho)*theta_raw[1] + sqrt(rho/scaling_factor)*phi);
+    // psi = tau[1]*(sqrt(rho)*theta_raw[1] + sqrt(1-rho)*scaling_factor*phi);
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
@@ -77,7 +77,7 @@ model {
   // likelihoods
   if (family == 1) {
     eta = linkinv_gauss(eta, link);
-    target+= normal_lpdf(y_real | eta, nu[1]);
+    target+= normal_lpdf(y_real | eta, sigma[1]);
   }
   else if (family == 2) {
     eta = linkinv_count(eta, link);
@@ -108,35 +108,35 @@ model {
   }
   if (mod == 2) { // BYM
     target+= normal_lpdf(theta_raw[1] | 0, 1);
-    target+= beta_lpdf(tau | shape1_tau, shape2_tau);
+    target+= beta_lpdf(rho | shape1_tau, shape2_tau);
     if (prior_dist_aux == 1)
-      target+= normal_lpdf(sigma | prior_mean_aux, prior_scale_aux);
+      target+= normal_lpdf(tau | prior_mean_aux, prior_scale_aux);
     else if (prior_dist_aux == 2)
-      target+= student_t_lpdf(sigma | prior_df_aux, prior_mean_aux, prior_scale_aux);
+      target+= student_t_lpdf(tau | prior_df_aux, prior_mean_aux, prior_scale_aux);
     else if (prior_dist_aux == 3)
-      target+= cauchy_lpdf(sigma | prior_mean_aux, prior_scale_aux);
+      target+= cauchy_lpdf(tau | prior_mean_aux, prior_scale_aux);
     else if (prior_dist_aux == 4)
-      target+= exponential_lpdf(sigma | prior_rate_aux);
+      target+= exponential_lpdf(tau | prior_rate_aux);
     /* else prior_dist_aux is 0 and nothing is added */
   }
   else {
     if (prior_dist_tau == 1)
-      target+= normal_lpdf(tau | prior_mean_tau, prior_scale_tau);
+      target+= normal_lpdf(rho | prior_mean_tau, prior_scale_tau);
     else if (prior_dist_tau == 2)
-      target+= student_t_lpdf(tau | prior_df_tau, prior_mean_tau, prior_scale_tau);
+      target+= student_t_lpdf(rho | prior_df_tau, prior_mean_tau, prior_scale_tau);
     else if (prior_dist_tau == 3)
-      target+= cauchy_lpdf(tau | prior_mean_tau, prior_scale_tau);
+      target+= cauchy_lpdf(rho | prior_mean_tau, prior_scale_tau);
     else if (prior_dist_tau == 4)
-      target+= exponential_lpdf(tau | prior_rate_tau);
+      target+= exponential_lpdf(rho | prior_rate_tau);
     /* else prior_dist_tau is 0 and nothing is added */
   }
   if (family == 1) { // prior on sd if outcome is gaussian
     if (prior_dist_nu == 1)
-      target+= normal_lpdf(nu[1] | prior_mean_nu, prior_scale_nu);
+      target+= normal_lpdf(sigma[1] | prior_mean_nu, prior_scale_nu);
     else if (prior_dist_nu == 2)
-      target+= student_t_lpdf(nu[1] | prior_df_nu, prior_mean_nu, prior_scale_nu);
+      target+= student_t_lpdf(sigma[1] | prior_df_nu, prior_mean_nu, prior_scale_nu);
     else if (prior_dist_nu == 3)
-      target+= cauchy_lpdf(nu[1] | prior_mean_nu, prior_scale_nu);
+      target+= cauchy_lpdf(sigma[1] | prior_mean_nu, prior_scale_nu);
     /* else prior_dist_nu is 0 and nothing is added */
   }
 }
@@ -155,7 +155,7 @@ generated quantities {
     }
     if (family == 1) {
       eta = linkinv_gauss(eta, link);
-      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], nu[1]);
+      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], sigma[1]);
     }
     else if (family == 2) {
       eta = linkinv_count(eta, link);

From fcc8d713585c5245ab88362e03fbdcc34b298f12 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 6 Aug 2017 20:52:57 -0400
Subject: [PATCH 11/59] get prior summary working for CAR models

---
 R/prior_summary.R    |  24 +++++
 R/stan_besag.R       |   8 +-
 R/stan_bym.R         |   4 +-
 R/stan_glm.fit.R     |   2 +-
 R/stan_spatial.fit.R | 213 ++++++++++++++++++++++++++++---------------
 exec/spatial.stan    |  68 ++++++--------
 6 files changed, 199 insertions(+), 120 deletions(-)

diff --git a/R/prior_summary.R b/R/prior_summary.R
index 0ea372737..c8282872a 100644
--- a/R/prior_summary.R
+++ b/R/prior_summary.R
@@ -176,6 +176,26 @@ print.prior_summary.stanreg <- function(x, digits, ...) {
                ", rate = ", .fr2(p$rate), ")"))
   }
   
+  # unique to stan_besag/stan_bym
+  if (!is.null(x[["prior_sigma"]]))
+    .print_scalar_prior(
+      x[["prior_sigma"]], 
+      txt = paste0("\nSigma", if (!sparse) " (after predictors centered)"), 
+      formatters
+    )
+  if (!is.null(x[["prior_rho"]]))
+    .print_scalar_prior(
+      x[["prior_rho"]], 
+      txt = paste0("\nRho", if (!sparse) " (after predictors centered)"), 
+      formatters
+    )
+  if (!is.null(x[["prior_tau"]]))
+    .print_scalar_prior(
+      x[["prior_tau"]], 
+      txt = paste0("\nTau", if (!sparse) " (after predictors centered)"), 
+      formatters
+    )
+  
   cat("\n------\n")
   cat("See help('prior_summary.stanreg') for more details")
   invisible(x)
@@ -224,11 +244,15 @@ used.sparse <- function(x) {
   stopifnot(length(formatters) == 2)
   .f1 <- formatters[[1]]
   .f2 <- formatters[[2]]
+
   cat(paste0("\n", txt, "\n ~"),
       if (is.na(p$dist)) {
         "flat"
       } else if (p$dist == "exponential") {
         paste0(p$dist,"(rate = ", .f1(p$rate), ")")
+      } else if (p$dist == "beta") {
+        paste0(p$dist,"(shape1 = ", .f1(p$shape1),
+               ", shape2 = ", .f1(p$shape1), ")")
       } else { # normal, student_t, cauchy
         if (is.null(p$df)) {
           paste0(p$dist,"(location = ", .f1(p$location), 
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 4c6e072c7..4e9d66ce9 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -28,8 +28,8 @@ stan_besag <- function(formula,
                      trials = NULL,
                      W,
                      ...,
-                     prior = normal(), prior_intercept = normal(), prior_tau = normal(),
-                     prior_nu = NULL,
+                     prior = normal(), prior_intercept = normal(),
+                     prior_sigma = NULL, prior_rho = NULL,
                      prior_PD = FALSE,
                      algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                      adapt_delta = NULL,
@@ -51,8 +51,8 @@ stan_besag <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_tau = prior_tau,
-                              prior_nu = prior_nu,
+                              prior_sigma = prior_sigma,
+                              prior_rho = prior_rho,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index aadd987d5..187bed452 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -29,7 +29,7 @@ stan_bym <- function(formula,
                         W,
                         ...,
                         prior = normal(), prior_intercept = normal(),
-                        prior_sigma = normal(), prior_tau = beta(), prior_nu = NULL,
+                        prior_sigma = NULL, prior_rho = beta(0.5,0.5), prior_tau = NULL,
                         prior_PD = FALSE,
                         algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                         adapt_delta = NULL,
@@ -50,7 +50,7 @@ stan_bym <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_sigma = prior_sigma, prior_tau = prior_tau, prior_nu = prior_nu,
+                              prior_sigma = prior_sigma, prior_rho = prior_rho, prior_tau = prior_tau,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_glm.fit.R b/R/stan_glm.fit.R
index 829971c6f..471682c36 100644
--- a/R/stan_glm.fit.R
+++ b/R/stan_glm.fit.R
@@ -242,7 +242,7 @@ stan_glm.fit <-
   
   if (length(weights) > 0 && all(weights == 1)) weights <- double()
   if (length(offset)  > 0 && all(offset  == 0)) offset  <- double()
-  
+
   # create entries in the data block of the .stan file
   standata <- nlist(
     N = nrow(xtemp),
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 5bfb9f85a..0ee92e10e 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -24,7 +24,7 @@ stan_spatial.fit <- function(x, y, w,
                              stan_function = c("stan_besag", "stan_bym"),
                              ...,
                              prior = normal(), prior_intercept = normal(),
-                             prior_sigma = NULL, prior_tau = NULL, prior_nu = NULL,
+                             prior_sigma = NULL, prior_rho = NULL, prior_tau = NULL,
                              prior_PD = FALSE,
                              algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                              adapt_delta = NULL,
@@ -100,50 +100,71 @@ stan_spatial.fit <- function(x, y, w,
   for (i in names(prior_intercept_stuff)) # prior_{dist, mean, scale, df, autoscale}_for_intercept
     assign(i, prior_intercept_stuff[[i]])
   
-  # Deal with prior_tau and prior_sigma
+  # Deal with prior_rho and prior_sigma
   if (stan_function == "stan_bym") {
-    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, family$link, default_scale = 1, 
-                                          ok_dists = ok_scale_dists)
-    names(prior_sigma_stuff) <- paste0(names(prior_sigma_stuff), 
-                                           "_for_aux")
-    for (i in names(prior_sigma_stuff)) # prior_{dist, mean, scale, df, autoscale}
-      assign(i, prior_sigma_stuff[[i]])
-    
-    prior_tau_stuff <- list(alpha = prior_tau$alpha, beta = prior_tau$beta)
-    prior_dist_for_tau <- 0
-    prior_mean_for_tau <- 0
-    prior_scale_for_tau <- 1
-    prior_df_for_tau <- 1
-  }
-  else if (stan_function == "stan_besag") {
+    has_tau <- 1
     prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, family$link, default_scale = 1, 
                                           ok_dists = ok_scale_dists)
     names(prior_tau_stuff) <- paste0(names(prior_tau_stuff), 
-                                       "_for_tau")
+                                           "_for_tau")
     for (i in names(prior_tau_stuff)) # prior_{dist, mean, scale, df, autoscale}
       assign(i, prior_tau_stuff[[i]])
-    prior_tau_stuff$alpha <- 0
-    prior_tau_stuff$beta <- 0
-    prior_dist_for_aux <- 0
-    prior_mean_for_aux <- 0
-    prior_scale_for_aux <- 1
-    prior_df_for_aux <- 1
+    # In BYM rho is constrained so appropriate prior is Beta dist.
+    if (is.null(prior_rho)) {
+      prior_dist_for_rho <- 0
+      prior_rho$alpha <- 1
+      prior_rho$beta <- 1
+      prior_dist_name_for_rho <- NA
+    }
+    else {
+      prior_dist_for_rho <- 1
+      prior_dist_name_for_rho <- "beta"
+    }
+
+    prior_mean_for_rho <- 0
+    prior_scale_for_rho <- 1
+    prior_df_for_rho <- 1
+    prior_rho_stuff <- list(prior_dist_name_for_rho = prior_dist_name_for_rho,
+                            shape1 = prior_rho$alpha,
+                            shape2 = prior_rho$beta,
+                            prior_mean_for_rho = prior_mean_for_rho,
+                            prior_scale_for_rho = prior_scale_for_rho,
+                            prior_df_for_rho = prior_df_for_rho
+                            )
+  }
+  else if (stan_function == "stan_besag") {
+    has_tau <- 0
+    prior_rho_stuff <- handle_glm_prior(prior_rho, nvars = 1, family$link, default_scale = 1, 
+                                          ok_dists = ok_scale_dists)
+    names(prior_rho_stuff) <- paste0(names(prior_rho_stuff), 
+                                       "_for_rho")
+    for (i in names(prior_rho_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_rho_stuff[[i]])
+
+    prior_rho_stuff$shape1 <- 1
+    prior_rho_stuff$shape2 <- 1
+    prior_dist_for_tau <- 0
+    prior_mean_for_tau <- 0
+    prior_scale_for_tau <- 1
+    prior_df_for_tau <- 1
   }
 
-  # Deal with prior_nu... needs more dist options
+  # Deal with prior_sigma... needs more dist options
   if (family$family == "gaussian") {
-    prior_nu_stuff <- handle_glm_prior(prior_nu, nvars = 1, family$link, default_scale = 1, 
+    has_sigma <- 1
+    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, family$link, default_scale = 1, 
                                     ok_dists = ok_dists)
-    names(prior_nu_stuff) <- paste0(names(prior_nu_stuff), 
-                                     "_for_nu")
-    for (i in names(prior_nu_stuff)) # prior_{dist, mean, scale, df, autoscale}
-      assign(i, prior_nu_stuff[[i]])
+    names(prior_sigma_stuff) <- paste0(names(prior_sigma_stuff), 
+                                     "_for_sigma")
+    for (i in names(prior_sigma_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_sigma_stuff[[i]])
   }
   else {
-    prior_dist_for_nu <- 0
-    prior_mean_for_nu <- 0
-    prior_scale_for_nu <- 1
-    prior_df_for_nu <- 1
+    has_sigma <- 0
+    prior_dist_for_sigma <- 0
+    prior_mean_for_sigma <- 0
+    prior_scale_for_sigma <- 1
+    prior_df_for_sigma <- 1
   }
   
   # QR decomposition for x
@@ -182,30 +203,28 @@ stan_spatial.fit <- function(x, y, w,
                     y_real = y_real,
                     y_int = y_int,
                     trials = trials,
-                    shape1_tau = c(prior_tau_stuff$alpha),
-                    shape2_tau = c(prior_tau_stuff$beta),
+                    shape1_rho = c(prior_rho_stuff$shape1),
+                    shape2_rho = c(prior_rho_stuff$shape2),
                     prior_dist_for_intercept = prior_dist_for_intercept,
                     prior_dist = prior_dist,
+                    prior_dist_rho = prior_dist_for_rho,
                     prior_dist_tau = prior_dist_for_tau,
-                    prior_dist_aux = prior_dist_for_aux,
-                    prior_dist_nu = prior_dist_for_nu,
+                    prior_dist_sigma = prior_dist_for_sigma,
                     prior_mean_for_intercept = c(prior_mean_for_intercept),
                     prior_scale_for_intercept = c(prior_scale_for_intercept),
                     prior_df_for_intercept = c(prior_df_for_intercept),
                     prior_mean = as.array(prior_mean),
                     prior_scale = as.array(prior_scale),
                     prior_df = c(prior_df),
-                    prior_mean_aux = c(prior_mean_for_aux),
-                    prior_scale_aux = c(prior_scale_for_aux),
-                    prior_df_aux = c(prior_df_for_aux),
-                    prior_rate_aux = 0,  # FIX ME!!!
                     prior_mean_tau = c(prior_mean_for_tau),
                     prior_scale_tau = c(prior_scale_for_tau),
                     prior_df_tau = c(prior_df_for_tau),
-                    prior_rate_tau = 0,  # FIX ME!!!
-                    prior_mean_nu = c(prior_mean_for_nu),
-                    prior_scale_nu = c(prior_scale_for_nu),
-                    prior_df_nu = c(prior_df_for_nu),
+                    prior_mean_rho = c(prior_mean_for_rho),
+                    prior_scale_rho = c(prior_scale_for_rho),
+                    prior_df_rho = c(prior_df_for_rho),
+                    prior_mean_sigma = c(prior_mean_for_sigma),
+                    prior_scale_sigma = c(prior_scale_for_sigma),
+                    prior_df_sigma = c(prior_df_for_sigma),
                     has_intercept = has_intercept,
                     mod = mod)
   standata$X <- array(standata$X, dim = c(standata$N, standata$K))
@@ -234,16 +253,19 @@ stan_spatial.fit <- function(x, y, w,
   prior_info <- summarize_spatial_prior(
     user_prior = prior_stuff,
     user_prior_intercept = prior_intercept_stuff,
+    user_prior_rho = prior_rho_stuff,
+    user_prior_sigma = prior_sigma_stuff,
     user_prior_tau = prior_tau_stuff,
-    user_prior_nu = prior_nu_stuff,
-    user_prior_aux = prior_aux_stuff,
     has_intercept = has_intercept,
     has_predictors = nvars > 0,
+    has_sigma = has_sigma,
+    has_rho = 1,
+    has_tau = has_tau,
     adjusted_prior_scale = prior_scale,
     adjusted_prior_intercept_scale = prior_scale_for_intercept,
-    adjusted_prior_scale_tau = prior_scale_for_tau,
-    adjusted_prior_scale_nu = prior_scale_for_nu,
-    adjusted_prior_scale_aux = prior_scale_for_aux)
+    adjusted_prior_scale_rho = prior_scale_for_rho,
+    adjusted_prior_scale_sigma = prior_scale_for_sigma,
+    adjusted_prior_scale_tau = prior_scale_for_tau)
 
   stanfit <- stanmodels$spatial
 
@@ -284,16 +306,19 @@ stan_spatial.fit <- function(x, y, w,
 
 summarize_spatial_prior <- function(user_prior,
                                     user_prior_intercept,
+                                    user_prior_sigma,
+                                    user_prior_rho,
                                     user_prior_tau,
-                                    user_prior_nu,
-                                    user_prior_aux,
                                     has_intercept,
                                     has_predictors,
+                                    has_sigma,
+                                    has_rho,
+                                    has_tau,
                                     adjusted_prior_scale,
                                     adjusted_prior_intercept_scale,
-                                    adjusted_prior_scale_tau,
-                                    adjusted_prior_scale_nu,
-                                    adjusted_prior_scale_aux) {
+                                    adjusted_prior_scale_sigma,
+                                    adjusted_prior_scale_rho,
+                                    adjusted_prior_scale_tau) {
   rescaled_coef <-
     user_prior$prior_autoscale && has_predictors &&
     !is.na(user_prior$prior_dist_name) &&
@@ -318,6 +343,33 @@ summarize_spatial_prior <- function(user_prior,
       user_prior_intercept$prior_dist_name_for_intercept <- "student_t"
     }
   }
+  if (has_sigma &&
+      user_prior_sigma$prior_dist_name_for_sigma %in% "t") {
+    if (all(user_prior_sigma$prior_df_for_sigma == 1)) {
+      user_prior_sigma$prior_dist_name_for_sigma <- "cauchy"
+    } else {
+      user_prior_sigma$prior_dist_name_for_sigma <- "student_t"
+    }
+  }
+
+  if (has_rho &&
+      user_prior_rho$prior_dist_name_for_rho %in% "t") {
+    if (all(user_prior_rho$prior_df_for_rho == 1)) {
+      user_prior_rho$prior_dist_name_for_rho <- "cauchy"
+    } else if (has_rho && user_prior_rho$prior_dist_name_for_rho == "beta") {
+      user_prior_rho$prior_dist_name_for_rho <- "beta"
+    } else {
+      user_prior_rho$prior_dist_name_for_rho <- "student_t"
+    }
+  }
+  if (has_tau &&
+      user_prior_tau$prior_dist_name_for_tau %in% "t") {
+    if (all(user_prior_tau$prior_df_for_tau == 1)) {
+      user_prior_tau$prior_dist_name_for_tau <- "cauchy"
+    } else {
+      user_prior_tau$prior_dist_name_for_tau <- "student_t"
+    }
+  }
   prior_list <- list(
     prior = 
       if (!has_predictors) NULL else with(user_prior, list(
@@ -339,24 +391,39 @@ summarize_spatial_prior <- function(user_prior,
           adjusted_prior_intercept_scale else NULL,
         df = if (prior_dist_name_for_intercept %in% "student_t")
           prior_df_for_intercept else NULL
-      ))#,
-    #prior_aux = 
-    #  if (!has_phi) NULL else with(user_prior_aux, list(
-    #    dist = prior_dist_name_for_aux,
-    #    location = if (!is.na(prior_dist_name_for_aux) && 
-    #                   prior_dist_name_for_aux != "exponential")
-    #      prior_mean_for_aux else NULL,
-    #    scale = if (!is.na(prior_dist_name_for_aux) && 
-    #                prior_dist_name_for_aux != "exponential")
-    #      prior_scale_for_aux else NULL,
-    #    df = if (!is.na(prior_dist_name_for_aux) && 
-    #             prior_dist_name_for_aux %in% "student_t")
-    #      prior_df_for_aux else NULL, 
-    #    rate = if (!is.na(prior_dist_name_for_aux) && 
-    #               prior_dist_name_for_aux %in% "exponential")
-    #      1 / prior_scale_for_aux else NULL,
-    #    aux_name = "phi"
-    #  ))
+      )),
+    prior_sigma = 
+      if (!has_sigma) NULL else with(user_prior_sigma, list(
+        dist = prior_dist_name_for_sigma,
+        location = prior_mean_for_sigma,
+        scale = prior_scale_for_sigma,
+        adjusted_scale = if (rescaled_int)
+          adjusted_prior_sigma_scale else NULL,
+        df = if (prior_dist_name_for_sigma %in% "student_t")
+          prior_df_for_sigma else NULL
+      )),
+    prior_rho = 
+      if (!has_rho) NULL else with(user_prior_rho, list(
+        dist = prior_dist_name_for_rho,
+        location = prior_mean_for_rho,
+        scale = prior_scale_for_rho,
+        shape1 = shape1,
+        shape2 = shape2,
+        adjusted_scale = if (rescaled_int)
+          adjusted_prior_rho_scale else NULL,
+        df = if (prior_dist_name_for_rho %in% "student_t")
+          prior_df_for_rho else NULL
+      )),
+    prior_tau = 
+      if (!has_tau) NULL else with(user_prior_tau, list(
+        dist = prior_dist_name_for_tau,
+        location = prior_mean_for_tau,
+        scale = prior_scale_for_tau,
+        adjusted_scale = if (rescaled_int)
+          adjusted_prior_tau_scale else NULL,
+        df = if (prior_dist_name_for_tau %in% "student_t")
+          prior_df_for_tau else NULL
+      ))
   )
   return(prior_list)
 }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 086899d2c..f708590c1 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -16,33 +16,31 @@ data {
   int link;
   int E_n;                        // number of adjacency pairs
   int edges[E_n, 2];              // adjacency pairs
-  real<lower=0> shape1_tau;        // priors
-  real<lower=0> shape2_tau;        // priors
+  real<lower=0> shape1_rho;        // priors
+  real<lower=0> shape2_rho;        // priors
   int<lower=0,upper=1> has_intercept;
   int<lower=1,upper=2> mod;       // 1 = besag (icar); 2 = bym
   real scaling_factor;
   int<lower=0> prior_dist_for_intercept;
   int<lower=0> prior_dist;
-  int<lower=0> prior_dist_aux;
   int<lower=0> prior_dist_tau;
-  int<lower=0> prior_dist_nu;
+  int<lower=0> prior_dist_rho;
+  int<lower=0> prior_dist_sigma;
   real prior_mean_for_intercept;
   real<lower=0> prior_scale_for_intercept;
   real<lower=0> prior_df_for_intercept;
   vector[K] prior_mean;
   vector<lower=0>[K] prior_scale;
   real<lower=0> prior_df[K];
-  real prior_mean_aux;
-  real<lower=0> prior_scale_aux;
-  real<lower=0> prior_df_aux;
-  real<lower=0> prior_rate_aux;
   real prior_mean_tau;
   real<lower=0> prior_scale_tau;
   real<lower=0> prior_df_tau;
-  real<lower=0> prior_rate_tau;
-  real prior_mean_nu;
-  real<lower=0> prior_scale_nu;
-  real<lower=0> prior_df_nu;
+  real prior_mean_rho;
+  real<lower=0> prior_scale_rho;
+  real<lower=0> prior_df_rho;
+  real prior_mean_sigma;
+  real<lower=0> prior_scale_sigma;
+  real<lower=0> prior_df_sigma;
 }
 transformed data {
   real poisson_max = 30 * log(2);
@@ -102,42 +100,32 @@ model {
       target+= normal_lpdf(beta | prior_mean, prior_scale);
     else if (prior_dist == 2)
       target+= student_t_lpdf(beta | prior_df, prior_mean, prior_scale);
-    else if (prior_dist == 3)
-      target+= cauchy_lpdf(beta | prior_mean, prior_scale);
     /* else prior_dist is 0 and nothing is added */
   }
   if (mod == 2) { // BYM
-    target+= normal_lpdf(theta_raw[1] | 0, 1);
-    target+= beta_lpdf(rho | shape1_tau, shape2_tau);
-    if (prior_dist_aux == 1)
-      target+= normal_lpdf(tau | prior_mean_aux, prior_scale_aux);
-    else if (prior_dist_aux == 2)
-      target+= student_t_lpdf(tau | prior_df_aux, prior_mean_aux, prior_scale_aux);
-    else if (prior_dist_aux == 3)
-      target+= cauchy_lpdf(tau | prior_mean_aux, prior_scale_aux);
-    else if (prior_dist_aux == 4)
-      target+= exponential_lpdf(tau | prior_rate_aux);
-    /* else prior_dist_aux is 0 and nothing is added */
-  }
-  else {
+    target+= normal_lpdf(theta_raw[1] | 0, 1);  // unstructured (random) effect
+    if (prior_dist_rho == 1)
+      target+= beta_lpdf(rho | shape1_rho, shape2_rho);
+    /* else prior_dist_rho is 0 and nothing is added */
     if (prior_dist_tau == 1)
-      target+= normal_lpdf(rho | prior_mean_tau, prior_scale_tau);
+      target+= normal_lpdf(tau | prior_mean_tau, prior_scale_tau);
     else if (prior_dist_tau == 2)
-      target+= student_t_lpdf(rho | prior_df_tau, prior_mean_tau, prior_scale_tau);
-    else if (prior_dist_tau == 3)
-      target+= cauchy_lpdf(rho | prior_mean_tau, prior_scale_tau);
-    else if (prior_dist_tau == 4)
-      target+= exponential_lpdf(rho | prior_rate_tau);
+      target+= student_t_lpdf(tau | prior_df_tau, prior_mean_tau, prior_scale_tau);
     /* else prior_dist_tau is 0 and nothing is added */
   }
+  else { // besag
+    if (prior_dist_rho == 1)
+      target+= normal_lpdf(rho | prior_mean_rho, prior_scale_rho);
+    else if (prior_dist_rho == 2)
+      target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
+    /* else prior_dist_rho is 0 and nothing is added */
+  }
   if (family == 1) { // prior on sd if outcome is gaussian
-    if (prior_dist_nu == 1)
-      target+= normal_lpdf(sigma[1] | prior_mean_nu, prior_scale_nu);
-    else if (prior_dist_nu == 2)
-      target+= student_t_lpdf(sigma[1] | prior_df_nu, prior_mean_nu, prior_scale_nu);
-    else if (prior_dist_nu == 3)
-      target+= cauchy_lpdf(sigma[1] | prior_mean_nu, prior_scale_nu);
-    /* else prior_dist_nu is 0 and nothing is added */
+    if (prior_dist_sigma == 1)
+      target+= normal_lpdf(sigma[1] | prior_mean_sigma, prior_scale_sigma);
+    else if (prior_dist_sigma == 2)
+      target+= student_t_lpdf(sigma[1] | prior_df_sigma, prior_mean_sigma, prior_scale_sigma);
+    /* else prior_dist_sigma is 0 and nothing is added */
   }
 }
 generated quantities {

From 34f03e6bbf84bcdad9487251056cb8716612c949 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 00:59:05 -0400
Subject: [PATCH 12/59] adding documentation for CAR models

---
 R/datasets.R       |  17 +++++++++++-
 R/stan_besag.R     |  62 +++++++++++++++++++++++++++++++++++++++++--
 R/stan_bym.R       |  64 ++++++++++++++++++++++++++++++++++++++++++---
 data/lattice10.rda | Bin 0 -> 3755 bytes
 4 files changed, 137 insertions(+), 6 deletions(-)
 create mode 100644 data/lattice10.rda

diff --git a/R/datasets.R b/R/datasets.R
index fb10cf192..58e1aa8d2 100644
--- a/R/datasets.R
+++ b/R/datasets.R
@@ -20,7 +20,7 @@
 #' Small datasets for use in \pkg{rstanarm} examples and vignettes.
 #'
 #' @name rstanarm-datasets
-#' @aliases kidiq roaches wells bball1970 bball2006 mortality tumors radon
+#' @aliases kidiq roaches wells bball1970 bball2006 mortality tumors radon lattice10
 #' @format 
 #' \describe{
 #' \item{\code{bball1970}}{
@@ -139,6 +139,21 @@
 #' \item \code{educ} Years of education (head of household)
 #' }
 #' }
+#' \item{\code{lattice10}}{
+#' The \code{grid_sim} SpatialPolygonsDataFrame object is an artificial lattice
+#' that has been used to simulate spatial dependence as a Gaussian Markov Random
+#' Field (GMRF).
+#' 
+#' Souce: Simulated data
+#' 
+#' 100 obs. of 3 variables
+#' \itemize{
+#' \item \code{gmrf} GMRF data simulated using Cholesky decomposition sampling from
+#' the precision form of the Multivariate Normal distribution centered at zero.
+#' \item \code{r} X-position of a location on the grid (origin is at the bottom-left).
+#' \item \code{c} Y-position of a location on the grid (origin is at the bottom-left).
+#' }
+#' }
 #' }
 #' 
 #' @references 
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 4e9d66ce9..7f346d057 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -18,9 +18,67 @@
 #' Bayesian CAR Intrinsic Autoregressive models via Stan
 #'
 #' Spatial regression modeling with an intrinsic conditional autoregressive (ICAR) prior.
-#'
+
+#' @rdname stan_besag
 #' @export
 #' 
+#' @template return-stanreg-object
+#' @template return-stanfit-object
+#' @template see-also
+#' @template args-formula-data-subset
+#' @template args-same-as
+#' @template args-same-as-rarely
+#' @template args-x-y
+#' @template args-dots
+#' @template args-prior_intercept
+#' @template args-priors
+#' @template args-prior_PD
+#' @template args-algorithm
+#' @template args-adapt_delta
+#' @template args-QR
+#' 
+#' @param family Distribution associated with the outcome. Gaussian, Binomial,
+#' and Poisson families are supported.
+#' @param trials If \code{family = binomial()} then a vector of trials (equal
+#' in length to the outcome) must be declared.
+#' @param W An N-by-N spatial weight matrix.
+#' @param prior_rho The prior distribution on the variance of the non-centered structured (spatial) effect.
+#' @param prior_sigma The prior distribution on the standard deviation of the outcome if \code{family = gaussian()} is declared.
+#' 
+#' @details The \code{stan_besag} model is similar to the analogous model in R-INLA. However, instead of using the integrated Laplace approximation (INLA) method, full Bayesian estimation is performed (if \code{algorithm} is \code{"sampling"}) via MCMC. The model includes priors on the intercept, regression coefficients, and the relevant scale parameters. The \code{stan_besag} function calls the workhorse \code{stan_spatial.fit} function, but it is also possible to call the latter directly.
+#'   
+#' @seealso The vignette for \code{stan_besag}.
+#' 
+#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. arXiv preprint	arXiv:1601.01180.
+#' 
+#' @examples 
+#' ### Simulated Data on a Lattice
+#' 
+#' data("lattice10", package = "rstanarm")
+#' 
+#' # plot GMRF
+#' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
+#' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
+#'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
+#' 
+#' # Convert a spatial polygon to an N-by-N weight matrix
+#' sp2weightmatrix <- function(spatialpolygon) {
+#'   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+#' }
+#' 
+#' # convert spatial object to neighborhood matrix
+#' W <- sp2weightmatrix(grid_sim)
+#' # W_sparse <- Matrix(W, sparse = TRUE, dimnames = list(NULL,NULL)) 
+#' 
+#' # simulate predictor/outcome
+#' x <- rnorm(nrow(W), 3, 1)
+#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
+#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' 
+#' # fit the model
+#' fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' pp_check(fit_besag)
+#'
 
 stan_besag <- function(formula,
                      family = gaussian(),
@@ -29,7 +87,7 @@ stan_besag <- function(formula,
                      W,
                      ...,
                      prior = normal(), prior_intercept = normal(),
-                     prior_sigma = NULL, prior_rho = NULL,
+                     prior_sigma = NULL, prior_rho = normal(),
                      prior_PD = FALSE,
                      algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                      adapt_delta = NULL,
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 187bed452..e3552e6dd 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -17,10 +17,68 @@
 
 #' Bayesian CAR BYM models via Stan
 #'
-#' Spatial regression modeling with a conditional autoregressive (CAR) prior.
-#'
+#' Spatial regression modeling with the Besag, York, Mollie conditional autoregressive (CAR) prior.
+#' @rdname stan_bym
 #' @export
 #' 
+#' @template return-stanreg-object
+#' @template return-stanfit-object
+#' @template see-also
+#' @template args-formula-data-subset
+#' @template args-same-as
+#' @template args-same-as-rarely
+#' @template args-x-y
+#' @template args-dots
+#' @template args-prior_intercept
+#' @template args-priors
+#' @template args-prior_PD
+#' @template args-algorithm
+#' @template args-adapt_delta
+#' @template args-QR
+#' 
+#' @param family Distribution associated with the outcome. Gaussian, Binomial, and Poisson families are supported.
+#' @param trials If \code{family = binomial()} then a vector of trials (equal in length to the outcome) must be declared.
+#' @param W An N-by-N spatial weight matrix.
+#' @param prior_rho The prior on the proportion of the marginal variance that is explained by the structured (spatial) effect. The hyperparameter \code{rho} is in the unit interval so users have the option of declaring a Beta prior distribution or a flat prior. A prior distribution with more mass around 1 is analogous to the prior belief that there exists a strong spatial relationship on the graph. 
+#' @param prior_tau The prior on the marginal variance contribution of the structured (spatial) and unstructured (random) effect.
+#' @param prior_sigma The prior distribution on the standard deviation of the outcome if \code{family = gaussian()} is declared.
+#' 
+#' @details The \code{stan_bym} model is similar to the analogous model in R-INLA. However, instead of using the integrated Laplace approximation (INLA) method, full Bayesian estimation is performed (if \code{algorithm} is \code{"sampling"}) via MCMC. The model includes priors on the intercept, regression coefficients, and the relevant scale/mixing parameters. The \code{stan_bym} function calls the workhorse \code{stan_spatial.fit} function, but it is also possible to call the latter directly.
+#' 
+#' @seealso The vignette for \code{stan_bym}.
+#' 
+#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. arXiv preprint	arXiv:1601.01180.
+#' 
+#' Simpson, D., Rue, H., Martins, T.G., Riebler, A. and Sorbye, S.H. (2015). Penalising model component complexity: A principled, practical approach to constructing priors. arXiv preprint	arXiv:1403.4630. 
+#' 
+#' @examples 
+#' ### Simulated Data on a Lattice
+#' 
+#' data("lattice10", package = "rstanarm")
+#' 
+#' # plot GMRF
+#' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
+#' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
+#'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
+#' 
+#' # Convert a spatial polygon to an N-by-N weight matrix
+#' sp2weightmatrix <- function(spatialpolygon) {
+#'   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+#' }
+#' 
+#' # convert spatial object to neighborhood matrix
+#' W <- sp2weightmatrix(grid_sim)
+#' # W_sparse <- Matrix(W, sparse = TRUE, dimnames = list(NULL,NULL)) 
+#' 
+#' # simulate predictor/outcome
+#' x <- rnorm(nrow(W), 3, 1)
+#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
+#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' 
+#' # fit the model
+#' fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' pp_check(fit_besag)
+
 
 stan_bym <- function(formula,
                         family = gaussian(),
@@ -29,7 +87,7 @@ stan_bym <- function(formula,
                         W,
                         ...,
                         prior = normal(), prior_intercept = normal(),
-                        prior_sigma = NULL, prior_rho = beta(0.5,0.5), prior_tau = NULL,
+                        prior_sigma = NULL, prior_rho = beta(0.5,0.5), prior_tau = normal(),
                         prior_PD = FALSE,
                         algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
                         adapt_delta = NULL,
diff --git a/data/lattice10.rda b/data/lattice10.rda
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From 1869c9c68f71890f9144d9752d0e4f4c0833c42d Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 18:52:38 -0400
Subject: [PATCH 13/59] adding stan_besag tests; CAR models return 'terms'

---
 R/pp_data.R                      |   6 +-
 R/predict.R                      |   8 ++
 R/stan_besag.R                   |   4 +-
 R/stan_bym.R                     |   8 ++
 R/stan_spatial.fit.R             |   2 +-
 R/stanreg-methods.R              |   1 -
 tests/testthat/test_stan_besag.R | 160 +++++++++++++++++++++++++++++++
 tests/testthat/test_stan_bym.R   |  19 ++++
 8 files changed, 203 insertions(+), 5 deletions(-)
 create mode 100644 tests/testthat/test_stan_besag.R
 create mode 100644 tests/testthat/test_stan_bym.R

diff --git a/R/pp_data.R b/R/pp_data.R
index d21d4c9af..5521568f4 100644
--- a/R/pp_data.R
+++ b/R/pp_data.R
@@ -50,7 +50,9 @@ pp_data <-
       offset <- object$offset %ORifNULL% rep(0, nrow(x))
     return(nlist(x, offset))
   }
-
+  #if (!is.null(newdata) & is(object, "car")) {
+  #  return(nlist(x))
+  #}
   offset <- .pp_data_offset(object, newdata, offset)
   Terms <- delete.response(terms(object))
   m <- model.frame(Terms, newdata, xlev = object$xlevels)
@@ -59,7 +61,7 @@ pp_data <-
   x <- model.matrix(Terms, m, contrasts.arg = object$contrasts)
   if (is(object, "polr") && !is_scobit(object)) 
     x <- x[,colnames(x) != "(Intercept)", drop = FALSE]
-  
+
   nlist(x, offset)
 }
 
diff --git a/R/predict.R b/R/predict.R
index bac656691..c6441e4c8 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -69,6 +69,14 @@ predict.stanreg <- function(object,
   stanmat <- as.matrix.stanreg(object)
   beta <- stanmat[, seq_len(ncol(dat$x))]
   eta <- linear_predictor(beta, dat$x, dat$offset)
+  if (is(object, "car")) {
+    if (nrow(object$x) > nrow(dat$x))
+      stop("'newdata' is less than the number of spatial regions.")
+    psi_indx <- grep("psi", colnames(as.matrix(object$stanfit)))
+    psi <- as.matrix(object$stanfit)[,psi_indx]
+    psi <- unname(colMeans(psi))
+    eta <- eta + psi
+  }
   if (type == "response") {
     inverse_link <- linkinv(object)
     eta <- inverse_link(eta)
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 7f346d057..413f80d9b 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -99,6 +99,7 @@ stan_besag <- function(formula,
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
+  mt <- terms(formula, data = data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
   
@@ -120,7 +121,8 @@ stan_besag <- function(formula,
                x = X, y = Y,
                family,
                formula,
-               model = mf, 
+               model = mf,
+               terms = mt,
                call = match.call(),
                stan_function = stan_function)
  
diff --git a/R/stan_bym.R b/R/stan_bym.R
index e3552e6dd..804190e00 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -99,8 +99,15 @@ stan_bym <- function(formula,
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
+  mt <- terms(formula, data = data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
+  
+  if (!is.null(prior_rho)) {
+    if (prior_rho$dist != "beta")
+      stop("'prior_rho' must be either beta() or NULL.") 
+  }
+  
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
                               trials = trials,
                               family = family,
@@ -119,6 +126,7 @@ stan_bym <- function(formula,
                family,
                formula,
                model = mf, 
+               terms = mt,
                call = match.call(),
                stan_function = stan_function)
   
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 0ee92e10e..7dd3f43be 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -215,7 +215,7 @@ stan_spatial.fit <- function(x, y, w,
                     prior_df_for_intercept = c(prior_df_for_intercept),
                     prior_mean = as.array(prior_mean),
                     prior_scale = as.array(prior_scale),
-                    prior_df = c(prior_df),
+                    prior_df = as.array(prior_df),
                     prior_mean_tau = c(prior_mean_for_tau),
                     prior_scale_tau = c(prior_scale_for_tau),
                     prior_df_tau = c(prior_df_for_tau),
diff --git a/R/stanreg-methods.R b/R/stanreg-methods.R
index 878fc8eac..cf0c71083 100644
--- a/R/stanreg-methods.R
+++ b/R/stanreg-methods.R
@@ -368,7 +368,6 @@ terms.stanreg <- function(x, ..., fixed.only = TRUE, random.only = FALSE) {
     fixed.only <- FALSE
   if (fixed.only && random.only) 
     stop("'fixed.only' and 'random.only' can't both be TRUE.", call. = FALSE)
-  
   Terms <- attr(fr, "terms")
   if (fixed.only) {
     Terms <- terms.formula(formula(x, fixed.only = TRUE))
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
new file mode 100644
index 000000000..1401b0f49
--- /dev/null
+++ b/tests/testthat/test_stan_besag.R
@@ -0,0 +1,160 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+# tests can be run using devtools::test() or manually by loading testthat 
+# package and then running the code below possibly with options(mc.cores = 4).
+
+if (.Platform$OS.type != "windows" && require(betareg)) {
+  library(rstanarm)
+  SEED <- 12345
+  set.seed(SEED)
+  ITER <- 100
+  CHAINS <- 2
+  
+  context("stan_besag")
+  
+  source(file.path("helpers", "expect_stanreg.R"))
+  source(file.path("helpers", "SW.R"))
+  
+  data("lattice10", package = "rstanarm")
+  
+  # Convert a spatial polygon to an N-by-N weight matrix
+  sp2weightmatrix <- function(spatialpolygon) {
+    spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+  }
+  W <- sp2weightmatrix(grid_sim)
+  x <- rnorm(nrow(W), 2, 1)
+  spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
+  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+  spatial_data$y_pois <- rpois(nrow(W), exp(-1 + 1.4 * x + spatial_data$phi))
+  spatial_data$trials <- rep(10, nrow(W))
+  spatial_data$y_binom <- rbinom(nrow(W), spatial_data$trials, binomial(link = "logit")$linkinv(1 + 0.4 * x + spatial_data$phi))
+  
+  fit_besag <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, W = W, iter = 1e3, chains = 4,
+                          prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
+                          prior_sigma = normal(0,1))
+  
+  # test family/link combinations
+  test_that("family = 'gaussian' works", {
+    SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+    SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+                                   W = W, iter = ITER, chains = CHAINS))
+    expect_stanreg(fit_gauss_ident)
+    expect_stanreg(fit_gauss_log)
+    expect_stanreg(fit_gauss_inv)
+  })
+  test_that("family = 'binomial' works", {
+    SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "logit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                      family = binomial(link = "probit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                       family = binomial(link = "cauchit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                   family = binomial(link = "log"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                       family = binomial(link = "cloglog"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    expect_stanreg(fit_binom_logit)
+    expect_stanreg(fit_binom_probit)
+    expect_stanreg(fit_binom_cauchit)
+    expect_stanreg(fit_binom_log)
+    expect_stanreg(fit_binom_cloglog)
+    
+  })
+  test_that("family = 'poisson' works", {
+    SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+                                  W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+                                  W = W, iter = 10, chains = CHAINS))
+    expect_stanreg(fit_pois_log)
+    expect_stanreg(fit_pois_ident)
+    expect_stanreg(fit_pois_sqrt)
+  })
+  
+  # test QR 
+  test_that("QR errors when number of predictors is <= 1", {
+    expect_error(
+      stan_besag(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
+      "'QR' can only be specified when there are multiple predictors"
+    )
+  })
+  
+  test_that("QR works when number of x and/or z predictors is >= 1", {
+    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                                     W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    expect_stanreg(fit_besag)
+  })
+  
+  test_that("stan_besag works with QR = TRUE and algorithm = 'optimizing'", {
+
+  })
+  
+  test_that("loo/waic for stan_besag works", {
+    SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "logit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
+                               family = poisson(link = "log"),
+                               W = W, iter = ITER, chains = CHAINS))
+    loo(fit_gauss)
+    loo(fit_binom)
+    loo(fit_pois)
+  })
+  
+  test_that("posterior_predict works for stan_besag", {
+    SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                               family = binomial(link = "logit"),
+                               W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
+                              family = poisson(link = "log"),
+                              W = W, iter = ITER, chains = CHAINS))
+    preds_gauss <- posterior_predict(fit_gauss)
+    preds_binom <- posterior_predict(fit_binom)
+    preds_pois <- posterior_predict(fit_pois)
+  })
+  
+  test_that("predict works for stan_besag", {
+    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    pred_besag <- predict(fit_besag)
+    pred_new_besag <- predict(fit_besag, newdata = data.frame(x = rnorm(100, 2, 1)))
+  })
+  
+  test_that("predict errors if nrow(newdata) < number of spatial regions", {
+    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    pred_besag <- predict(fit_besag)
+    expect_error(pred_new_besag <- predict(fit_besag, newdata = data.frame(x = rnorm(10, 2, 1))),
+                 "'newdata' is less than the number of spatial regions.")
+  })
+  
+}
\ No newline at end of file
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
new file mode 100644
index 000000000..e1abf2384
--- /dev/null
+++ b/tests/testthat/test_stan_bym.R
@@ -0,0 +1,19 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+# tests can be run using devtools::test() or manually by loading testthat 
+# package and then running the code below possibly with options(mc.cores = 4).
\ No newline at end of file

From 44f7e4a92337081649725f8e1d47e29204e63c7d Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 23:14:16 -0400
Subject: [PATCH 14/59] adding vb methods for CAR models; optimizing currently
 fails

---
 R/stan_spatial.fit.R | 84 +++++++++++++++++++++++++++++---------------
 R/stanreg.R          |  1 +
 2 files changed, 56 insertions(+), 29 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 7dd3f43be..07856351b 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -268,38 +268,64 @@ stan_spatial.fit <- function(x, y, w,
     adjusted_prior_scale_tau = prior_scale_for_tau)
 
   stanfit <- stanmodels$spatial
-
-  if (algorithm == "sampling") {
-    sampling_args <- set_sampling_args(
-      object = stanfit, 
-      prior = prior, 
-      user_dots = list(...), 
-      user_adapt_delta = adapt_delta, 
-      data = standata, 
-      pars = pars, 
-      show_messages = FALSE)
-    stanfit <- do.call(sampling, sampling_args)
+  
+  if (algorithm == "optimizing") {
+    out <-
+      optimizing(stanfit,
+                 data = standata,
+                 draws = 1000,
+                 constrained = TRUE,
+                 ...)
+    check_stanfit(out)
+    out$par <- out$par[!grepl("(phi_raw|theta_raw)", names(out$par))]
+    new_names <- names(out$par)
+    mark <- grepl("^beta\\[[[:digit:]]+\\]$", new_names)
+    if (QR && ncol(xtemp) > 1) {
+      out$par[mark] <- R_inv %*% out$par[mark]
+      out$theta_tilde[,mark] <- out$theta_tilde[, mark] %*% t(R_inv)
+    }
+    new_names[mark] <- colnames(xtemp)
+    new_names[new_names == "alpha[1]"] <- "(Intercept)"
+    names(out$par) <- new_names
+    out$stanfit <- suppressMessages(sampling(stanfit, data = standata, chains = 0))
+    return(structure(out, prior.info = prior_info))
   }
   else {
-    stop(paste("algorithm", algorithm, "is not supported."))
-  }
-  check_stanfit(stanfit)
-
-  if (QR) {
-    thetas <- extract(stanfit, pars = "beta", inc_warmup = TRUE, 
-                      permuted = FALSE)
-    betas <- apply(thetas, 1:2, FUN = function(theta) R_inv %*% theta)
-    end <- tail(dim(betas), 1L)
-    for (chain in 1:end) for (param in 1:nrow(betas)) {
-      stanfit@sim$samples[[chain]][[has_intercept + param]] <-
-        if (ncol(xtemp) > 1) betas[param, , chain] else betas[param, chain]
-    } 
+    if (algorithm == "sampling") {
+      sampling_args <- set_sampling_args(
+        object = stanfit, 
+        prior = prior, 
+        user_dots = list(...), 
+        user_adapt_delta = adapt_delta, 
+        data = standata, 
+        pars = pars, 
+        show_messages = FALSE)
+      stanfit <- do.call(sampling, sampling_args)
+    }
+    else { # algorithm either "meanfield" or "fullrank"
+      stanfit <- rstan::vb(stanfit, pars = pars, data = standata,
+                           algorithm = algorithm, init = 0.001, ...)
+      if (!QR) 
+        recommend_QR_for_vb()
+    }
+    check_stanfit(stanfit)
+    
+    if (QR) {
+      thetas <- extract(stanfit, pars = "beta", inc_warmup = TRUE, 
+                        permuted = FALSE)
+      betas <- apply(thetas, 1:2, FUN = function(theta) R_inv %*% theta)
+      end <- tail(dim(betas), 1L)
+      for (chain in 1:end) for (param in 1:nrow(betas)) {
+        stanfit@sim$samples[[chain]][[has_intercept + param]] <-
+          if (ncol(xtemp) > 1) betas[param, , chain] else betas[param, chain]
+      } 
+    }
+    new_names <- c(if (has_intercept) "(Intercept)", 
+                   colnames(xtemp), "rho", if(mod == 2) c("tau"),
+                   if(family$family == "gaussian") "sigma", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
+    stanfit@sim$fnames_oi <- new_names
+    return(structure(stanfit, prior.info = prior_info))
   }
-  new_names <- c(if (has_intercept) "(Intercept)", 
-                 colnames(xtemp), "rho", if(mod == 2) c("tau"),
-                 if(family$family == "gaussian") "sigma", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
-  stanfit@sim$fnames_oi <- new_names
-  return(structure(stanfit, prior.info = prior_info))
 }
 
 # Summarize spatial prior
diff --git a/R/stanreg.R b/R/stanreg.R
index 1889605b2..ffee71f01 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -45,6 +45,7 @@ stanreg <- function(object) {
   if (opt) {
     stanmat <- stanfit$theta_tilde
     probs <- c(0.025, .975)
+    browser()
     stan_summary <- cbind(Median = apply(stanmat, 2L, median), 
                           MAD_SD = apply(stanmat, 2L, mad),
                           t(apply(stanmat, 2L, quantile, probs)))

From 874689318774daa62ebe3a9abcfea138cac320ca Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 23:23:35 -0400
Subject: [PATCH 15/59] adding tests for stan_bym

---
 tests/testthat/test_stan_besag.R |   4 -
 tests/testthat/test_stan_bym.R   | 139 ++++++++++++++++++++++++++++++-
 2 files changed, 138 insertions(+), 5 deletions(-)

diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 1401b0f49..6e28f7301 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -45,10 +45,6 @@ if (.Platform$OS.type != "windows" && require(betareg)) {
   spatial_data$trials <- rep(10, nrow(W))
   spatial_data$y_binom <- rbinom(nrow(W), spatial_data$trials, binomial(link = "logit")$linkinv(1 + 0.4 * x + spatial_data$phi))
   
-  fit_besag <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, W = W, iter = 1e3, chains = 4,
-                          prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
-                          prior_sigma = normal(0,1))
-  
   # test family/link combinations
   test_that("family = 'gaussian' works", {
     SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index e1abf2384..4320b4cb3 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -16,4 +16,141 @@
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 # tests can be run using devtools::test() or manually by loading testthat 
-# package and then running the code below possibly with options(mc.cores = 4).
\ No newline at end of file
+# package and then running the code below possibly with options(mc.cores = 4).
+
+if (.Platform$OS.type != "windows" && require(betareg)) {
+  library(rstanarm)
+  SEED <- 12345
+  set.seed(SEED)
+  ITER <- 50
+  CHAINS <- 2
+  
+  context("stan_bym")
+  
+  source(file.path("helpers", "expect_stanreg.R"))
+  source(file.path("helpers", "SW.R"))
+  
+  data("lattice10", package = "rstanarm")
+  
+  # Convert a spatial polygon to an N-by-N weight matrix
+  sp2weightmatrix <- function(spatialpolygon) {
+    spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+  }
+  W <- sp2weightmatrix(grid_sim)
+  x <- rnorm(nrow(W), 2, 1)
+  spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
+  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+  spatial_data$y_pois <- rpois(nrow(W), exp(-1 + 1.4 * x + spatial_data$phi))
+  spatial_data$trials <- rep(10, nrow(W))
+  spatial_data$y_binom <- rbinom(nrow(W), spatial_data$trials, binomial(link = "logit")$linkinv(1 + 0.4 * x + spatial_data$phi))
+  
+  # test family/link combinations
+  test_that("family = 'gaussian' works", {
+    SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+    SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+                                   W = W, iter = 10, chains = CHAINS))
+    expect_stanreg(fit_gauss_ident)
+    expect_stanreg(fit_gauss_log)
+    expect_stanreg(fit_gauss_inv)
+  })
+  test_that("family = 'binomial' works", {
+    SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "logit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                      family = binomial(link = "probit"),
+                                      W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                       family = binomial(link = "cauchit"),
+                                       W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                   family = binomial(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+    SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                       family = binomial(link = "cloglog"),
+                                       W = W, iter = ITER, chains = CHAINS))
+    expect_stanreg(fit_binom_logit)
+    expect_stanreg(fit_binom_probit)
+    expect_stanreg(fit_binom_cauchit)
+    expect_stanreg(fit_binom_log)
+    expect_stanreg(fit_binom_cloglog)
+    
+  })
+  test_that("family = 'poisson' works", {
+    SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+                                  W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+                                    W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+                                   W = W, iter = 10, chains = CHAINS))
+    expect_stanreg(fit_pois_log)
+    expect_stanreg(fit_pois_ident)
+    expect_stanreg(fit_pois_sqrt)
+  })
+  
+  # test QR 
+  test_that("QR errors when number of predictors is <= 1", {
+    expect_error(
+      stan_bym(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
+      "'QR' can only be specified when there are multiple predictors"
+    )
+  })
+  
+  test_that("QR works when number of x and/or z predictors is >= 1", {
+    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    expect_stanreg(fit_bym)
+  })
+  
+  test_that("stan_bym works with QR = TRUE and algorithm = 'optimizing'", {
+    
+  })
+  
+  test_that("loo/waic for stan_bym works", {
+    SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                               family = binomial(link = "logit"),
+                               W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
+                              family = poisson(link = "log"),
+                              W = W, iter = ITER, chains = CHAINS))
+    loo(fit_gauss)
+    loo(fit_binom)
+    loo(fit_pois)
+  })
+  
+  test_that("posterior_predict works for stan_bym", {
+    SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                               family = binomial(link = "logit"),
+                               W = W, iter = ITER, chains = CHAINS))
+    SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
+                              family = poisson(link = "log"),
+                              W = W, iter = ITER, chains = CHAINS))
+    preds_gauss <- posterior_predict(fit_gauss)
+    preds_binom <- posterior_predict(fit_binom)
+    preds_pois <- posterior_predict(fit_pois)
+  })
+  
+  test_that("predict works for stan_bym", {
+    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    pred_besag <- predict(fit_bym)
+    pred_new_besag <- predict(fit_bym, newdata = data.frame(x = rnorm(100, 2, 1)))
+  })
+  
+  test_that("predict errors if nrow(newdata) < number of spatial regions", {
+    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+    pred_besag <- predict(fit_bym)
+    expect_error(pred_new_besag <- predict(fit_bym, newdata = data.frame(x = rnorm(10, 2, 1))),
+                 "'newdata' is less than the number of spatial regions.")
+  })
+  
+}
\ No newline at end of file

From c329a6acb888a7636a115fa26e51933717ab7470 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 23:40:15 -0400
Subject: [PATCH 16/59] use sparse matrix ops for 'scaling_factor'

---
 R/stan_spatial.fit.R | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 07856351b..fcc03ab0f 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -230,23 +230,31 @@ stan_spatial.fit <- function(x, y, w,
   standata$X <- array(standata$X, dim = c(standata$N, standata$K))
 
   # create scaling_factor a la Dan Simpson
-  create_scaling_factor <- function(dat, W) {
+  create_scaling_factor <- function(dat) {
+    edges <- dat$edges
+    #Build the adjacency matrix
+    adj.matrix <- Matrix::sparseMatrix(i=edges[,1],j=edges[,2],x=1,symmetric=TRUE)
     #The ICAR precision matrix (note! This is singular)
-    Q <-  diag(rowSums(W), dat$N) - W
+    Q <-  Matrix::Diagonal(dat$N, Matrix::rowSums(adj.matrix)) - adj.matrix
+    
     #Add a small jitter to the diagonal for numerical stability (optional but recommended)
-    Q_pert <- Q + diag(dat$N) * max(diag(Q)) * sqrt(.Machine$double.eps)
+    Q_pert <- Q + Matrix::Diagonal(dat$N) * max(Matrix::diag(Q)) * sqrt(.Machine$double.eps)
     
     # Compute the diagonal elements of the covariance matrix subject to the 
     # constraint that the entries of the ICAR sum to zero.
-    #See the function help for further details.
+    # See the function help for further details.
     Q_inv <- inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
-    Q_inv <- as.matrix(Q_inv)
-    #Compute the geometric mean of the variances, which are on the diagonal of Q.inv
-    scaling_factor <- exp(mean(log(diag(Q_inv))))
+    
+    # Compute the geometric mean of the variances, which are on the diagonal of Q.inv
+    scaling_factor <- exp(mean(log(Matrix::diag(Q_inv))))
     return(scaling_factor)
   }
-  standata$scaling_factor <- create_scaling_factor(standata, W)
   
+  if (stan_function == "stan_bym")
+    standata$scaling_factor <- create_scaling_factor(standata)
+  else
+    standata$scaling_factor <- 0
+
   pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "sigma",
             "mean_PPD", "psi")
 

From 0ebd45cf2766d2331f4ef9261c1ae5ad492aaff0 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 7 Aug 2017 23:47:27 -0400
Subject: [PATCH 17/59] adding light documentation for stan_spatial.fit

---
 R/stan_spatial.fit.R | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index fcc03ab0f..03f5c2861 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -15,6 +15,12 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
+#' Workhorse function for CAR spatial models.
+#' 
+#' Both \code{stan_besag} and \code{stan_bym} call \code{stan_spatial.fit} to fit
+#' the appropriate spatial model. See the documentation for either model for further
+#' details on the arguments of \code{stan_spatial.fit}.
+#' 
 #' @export
 #' 
 

From 9859ddb7012cbf53cccf051421d2c8f484ff5cdb Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 8 Aug 2017 14:22:32 -0400
Subject: [PATCH 18/59] omit optimizing from supported algorithms in CAR models

---
 R/stan_besag.R       | 19 ++++++++++++------
 R/stan_bym.R         | 47 ++++++++++++++++++++++++++++++--------------
 R/stan_spatial.fit.R |  4 +++-
 exec/spatial.stan    |  2 ++
 4 files changed, 50 insertions(+), 22 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 413f80d9b..424fcbfdd 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -15,10 +15,10 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Bayesian CAR Intrinsic Autoregressive models via Stan
+#' Bayesian intrinsic conditional autoregressive (ICAR) models via Stan
 #'
 #' Spatial regression modeling with an intrinsic conditional autoregressive (ICAR) prior.
-
+#' 
 #' @rdname stan_besag
 #' @export
 #' 
@@ -45,11 +45,19 @@
 #' @param prior_rho The prior distribution on the variance of the non-centered structured (spatial) effect.
 #' @param prior_sigma The prior distribution on the standard deviation of the outcome if \code{family = gaussian()} is declared.
 #' 
-#' @details The \code{stan_besag} model is similar to the analogous model in R-INLA. However, instead of using the integrated Laplace approximation (INLA) method, full Bayesian estimation is performed (if \code{algorithm} is \code{"sampling"}) via MCMC. The model includes priors on the intercept, regression coefficients, and the relevant scale parameters. The \code{stan_besag} function calls the workhorse \code{stan_spatial.fit} function, but it is also possible to call the latter directly.
+#' @details The \code{stan_besag} model is similar to the analogous model in
+#'   R-INLA. However, instead of using the integrated Laplace approximation
+#'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm}
+#'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
+#'   regression coefficients, and the relevant scale parameters. The
+#'   \code{stan_besag} function calls the workhorse \code{stan_spatial.fit}
+#'   function, but it is also possible to call the latter directly.
 #'   
 #' @seealso The vignette for \code{stan_besag}.
 #' 
-#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. arXiv preprint	arXiv:1601.01180.
+#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An
+#'   intuitive Bayesian spatial model for disease mapping that accounts for
+#'   scaling. arXiv preprint	arXiv:1601.01180.
 #' 
 #' @examples 
 #' ### Simulated Data on a Lattice
@@ -79,7 +87,6 @@
 #' fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
 #' pp_check(fit_besag)
 #'
-
 stan_besag <- function(formula,
                      family = gaussian(),
                      data,
@@ -89,7 +96,7 @@ stan_besag <- function(formula,
                      prior = normal(), prior_intercept = normal(),
                      prior_sigma = NULL, prior_rho = normal(),
                      prior_PD = FALSE,
-                     algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                     algorithm = c("sampling", "meanfield", "fullrank"),
                      adapt_delta = NULL,
                      QR = FALSE) {
   stan_function <- "stan_besag"
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 804190e00..75a79ce57 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -15,10 +15,10 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Bayesian CAR BYM models via Stan
+#' Bayesian conditional autoregressive (CAR) BYM models via Stan
 #'
 #' Spatial regression modeling with the Besag, York, Mollie conditional autoregressive (CAR) prior.
-#' @rdname stan_bym
+#' 
 #' @export
 #' 
 #' @template return-stanreg-object
@@ -36,20 +36,39 @@
 #' @template args-adapt_delta
 #' @template args-QR
 #' 
-#' @param family Distribution associated with the outcome. Gaussian, Binomial, and Poisson families are supported.
-#' @param trials If \code{family = binomial()} then a vector of trials (equal in length to the outcome) must be declared.
+#' @param family Distribution associated with the outcome. Gaussian, Binomial,
+#'   and Poisson families are supported.
+#' @param trials If \code{family = binomial()} then a vector of trials (equal in
+#'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_rho The prior on the proportion of the marginal variance that is explained by the structured (spatial) effect. The hyperparameter \code{rho} is in the unit interval so users have the option of declaring a Beta prior distribution or a flat prior. A prior distribution with more mass around 1 is analogous to the prior belief that there exists a strong spatial relationship on the graph. 
-#' @param prior_tau The prior on the marginal variance contribution of the structured (spatial) and unstructured (random) effect.
-#' @param prior_sigma The prior distribution on the standard deviation of the outcome if \code{family = gaussian()} is declared.
-#' 
-#' @details The \code{stan_bym} model is similar to the analogous model in R-INLA. However, instead of using the integrated Laplace approximation (INLA) method, full Bayesian estimation is performed (if \code{algorithm} is \code{"sampling"}) via MCMC. The model includes priors on the intercept, regression coefficients, and the relevant scale/mixing parameters. The \code{stan_bym} function calls the workhorse \code{stan_spatial.fit} function, but it is also possible to call the latter directly.
+#' @param prior_rho The prior on the proportion of the marginal variance that is
+#'   explained by the structured (spatial) effect. The hyperparameter \code{rho}
+#'   is in the unit interval so users have the option of declaring a Beta prior 
+#'   distribution or a flat prior. A prior distribution with more mass around 1 
+#'   is analogous to the prior belief that there exists a strong spatial 
+#'   relationship on the graph.
+#' @param prior_tau The prior on the marginal variance contribution of the
+#'   structured (spatial) and unstructured (random) effect.
+#' @param prior_sigma The prior distribution on the standard deviation of the
+#'   outcome if \code{family = gaussian()} is declared.
+#'   
+#' @details The \code{stan_bym} model is similar to the analogous model in
+#'   R-INLA. However, instead of using the integrated Laplace approximation
+#'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm}
+#'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
+#'   regression coefficients, and the relevant scale/mixing parameters. The
+#'   \code{stan_bym} function calls the workhorse \code{stan_spatial.fit}
+#'   function, but it is also possible to call the latter directly.
 #' 
 #' @seealso The vignette for \code{stan_bym}.
 #' 
-#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. arXiv preprint	arXiv:1601.01180.
-#' 
-#' Simpson, D., Rue, H., Martins, T.G., Riebler, A. and Sorbye, S.H. (2015). Penalising model component complexity: A principled, practical approach to constructing priors. arXiv preprint	arXiv:1403.4630. 
+#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An
+#'   intuitive Bayesian spatial model for disease mapping that accounts for
+#'   scaling. arXiv preprint	arXiv:1601.01180.
+#'   
+#'   Simpson, D., Rue, H., Martins, T.G., Riebler, A. and Sorbye, S.H. (2015).
+#'   Penalising model component complexity: A principled, practical approach to
+#'   constructing priors. arXiv preprint	arXiv:1403.4630.
 #' 
 #' @examples 
 #' ### Simulated Data on a Lattice
@@ -78,8 +97,6 @@
 #' # fit the model
 #' fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
 #' pp_check(fit_besag)
-
-
 stan_bym <- function(formula,
                         family = gaussian(),
                         data,
@@ -89,7 +106,7 @@ stan_bym <- function(formula,
                         prior = normal(), prior_intercept = normal(),
                         prior_sigma = NULL, prior_rho = beta(0.5,0.5), prior_tau = normal(),
                         prior_PD = FALSE,
-                        algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                        algorithm = c("sampling", "meanfield", "fullrank"),
                         adapt_delta = NULL,
                         QR = FALSE) {
   stan_function <- "stan_bym"
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 03f5c2861..4fa48cf79 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -32,7 +32,7 @@ stan_spatial.fit <- function(x, y, w,
                              prior = normal(), prior_intercept = normal(),
                              prior_sigma = NULL, prior_rho = NULL, prior_tau = NULL,
                              prior_PD = FALSE,
-                             algorithm = c("sampling", "optimizing", "meanfield", "fullrank"),
+                             algorithm = c("sampling", "meanfield", "fullrank"),
                              adapt_delta = NULL,
                              QR = FALSE) {
   # check that W is appropriate
@@ -283,12 +283,14 @@ stan_spatial.fit <- function(x, y, w,
 
   stanfit <- stanmodels$spatial
   
+  # n.b. optimizing is not supported
   if (algorithm == "optimizing") {
     out <-
       optimizing(stanfit,
                  data = standata,
                  draws = 1000,
                  constrained = TRUE,
+                 hessian = TRUE,
                  ...)
     check_stanfit(out)
     out$par <- out$par[!grepl("(phi_raw|theta_raw)", names(out$par))]
diff --git a/exec/spatial.stan b/exec/spatial.stan
index f708590c1..e645ea239 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -3,6 +3,8 @@ functions {
   #include "continuous_likelihoods.stan"
   #include "binomial_likelihoods.stan"
   #include "count_likelihoods.stan"
+  // add negative_binomial likelihood
+  // add gamma likelihood
 }
 data {
   int<lower=0> N;                 // number of regions

From 7c36fefece1357e2a1d4ddc1bc67bf5cfa728f99 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 9 Aug 2017 00:04:53 -0400
Subject: [PATCH 19/59] use chunks stan code; use prior_aux

---
 R/stan_besag.R       |   5 +-
 R/stan_bym.R         |   6 +-
 R/stan_spatial.fit.R | 128 ++++++++++++++++++++++++---------------
 exec/spatial.stan    | 138 +++++++++++++++++++++++++++++++++++--------
 4 files changed, 198 insertions(+), 79 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 424fcbfdd..5d36a9b9f 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -43,7 +43,6 @@
 #' in length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
 #' @param prior_rho The prior distribution on the variance of the non-centered structured (spatial) effect.
-#' @param prior_sigma The prior distribution on the standard deviation of the outcome if \code{family = gaussian()} is declared.
 #' 
 #' @details The \code{stan_besag} model is similar to the analogous model in
 #'   R-INLA. However, instead of using the integrated Laplace approximation
@@ -94,7 +93,7 @@ stan_besag <- function(formula,
                      W,
                      ...,
                      prior = normal(), prior_intercept = normal(),
-                     prior_sigma = NULL, prior_rho = normal(),
+                     prior_aux = NULL, prior_rho = normal(),
                      prior_PD = FALSE,
                      algorithm = c("sampling", "meanfield", "fullrank"),
                      adapt_delta = NULL,
@@ -117,7 +116,7 @@ stan_besag <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_sigma = prior_sigma,
+                              prior_aux = prior_aux,
                               prior_rho = prior_rho,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 75a79ce57..546ca03a2 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -49,8 +49,6 @@
 #'   relationship on the graph.
 #' @param prior_tau The prior on the marginal variance contribution of the
 #'   structured (spatial) and unstructured (random) effect.
-#' @param prior_sigma The prior distribution on the standard deviation of the
-#'   outcome if \code{family = gaussian()} is declared.
 #'   
 #' @details The \code{stan_bym} model is similar to the analogous model in
 #'   R-INLA. However, instead of using the integrated Laplace approximation
@@ -104,7 +102,7 @@ stan_bym <- function(formula,
                         W,
                         ...,
                         prior = normal(), prior_intercept = normal(),
-                        prior_sigma = NULL, prior_rho = beta(0.5,0.5), prior_tau = normal(),
+                        prior_aux = NULL, prior_rho = beta(0.5,0.5), prior_tau = normal(),
                         prior_PD = FALSE,
                         algorithm = c("sampling", "meanfield", "fullrank"),
                         adapt_delta = NULL,
@@ -132,7 +130,7 @@ stan_bym <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_sigma = prior_sigma, prior_rho = prior_rho, prior_tau = prior_tau,
+                              prior_aux = prior_aux, prior_rho = prior_rho, prior_tau = prior_tau,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 4fa48cf79..d9b127051 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -30,7 +30,7 @@ stan_spatial.fit <- function(x, y, w,
                              stan_function = c("stan_besag", "stan_bym"),
                              ...,
                              prior = normal(), prior_intercept = normal(),
-                             prior_sigma = NULL, prior_rho = NULL, prior_tau = NULL,
+                             prior_aux = NULL, prior_rho = NULL, prior_tau = NULL,
                              prior_PD = FALSE,
                              algorithm = c("sampling", "meanfield", "fullrank"),
                              adapt_delta = NULL,
@@ -52,7 +52,7 @@ stan_spatial.fit <- function(x, y, w,
   
   if (is.null(trials))
     trials <- rep(0,length(y))
-  # Replace all this stuff with family = binomial(link = "logit") calls etc.
+
   if (family$family == "gaussian") {
     y_real <- y
     y_int <- rep(0, length(y))
@@ -73,7 +73,7 @@ stan_spatial.fit <- function(x, y, w,
       family_num <- 2
     }
     if(!is.integer(y_int))
-      stop("Outcome must an integer for count likelihoods.")
+      stop("Outcome must be an integer for count likelihoods.")
   }
   
   if (stan_function == "stan_besag")
@@ -87,7 +87,8 @@ stan_spatial.fit <- function(x, y, w,
     assign(i, x_stuff[[i]])
   nvars <- ncol(xtemp)
   
-  ok_dists <- nlist("normal", student_t = "t", "cauchy")
+  ok_dists <- nlist("normal", student_t = "t", "cauchy", "hs", "hs_plus", 
+                    "laplace", "lasso", "product_normal")
   ok_intercept_dists <- ok_dists
   ok_scale_dists <- nlist("normal", student_t = "t", "cauchy", "exponential")
 
@@ -155,22 +156,21 @@ stan_spatial.fit <- function(x, y, w,
     prior_df_for_tau <- 1
   }
 
-  # Deal with prior_sigma... needs more dist options
-  if (family$family == "gaussian") {
-    has_sigma <- 1
-    prior_sigma_stuff <- handle_glm_prior(prior_sigma, nvars = 1, family$link, default_scale = 1, 
+  if (family$family %in% c("gaussian","Gamma","neg_binomial_2")) {
+    has_aux <- 1
+    prior_aux_stuff <- handle_glm_prior(prior_aux, nvars = 1, family$link, default_scale = 1, 
                                     ok_dists = ok_dists)
-    names(prior_sigma_stuff) <- paste0(names(prior_sigma_stuff), 
-                                     "_for_sigma")
-    for (i in names(prior_sigma_stuff)) # prior_{dist, mean, scale, df, autoscale}
-      assign(i, prior_sigma_stuff[[i]])
+    names(prior_aux_stuff) <- paste0(names(prior_aux_stuff), 
+                                     "_for_aux")
+    for (i in names(prior_aux_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_aux_stuff[[i]])
   }
   else {
-    has_sigma <- 0
-    prior_dist_for_sigma <- 0
-    prior_mean_for_sigma <- 0
-    prior_scale_for_sigma <- 1
-    prior_df_for_sigma <- 1
+    has_aux <- 0
+    prior_dist_for_aux <- 0
+    prior_mean_for_aux <- 0
+    prior_scale_for_aux <- 1
+    prior_df_for_aux <- 1
   }
   
   # QR decomposition for x
@@ -215,7 +215,7 @@ stan_spatial.fit <- function(x, y, w,
                     prior_dist = prior_dist,
                     prior_dist_rho = prior_dist_for_rho,
                     prior_dist_tau = prior_dist_for_tau,
-                    prior_dist_sigma = prior_dist_for_sigma,
+                    prior_dist_for_aux = prior_dist_for_aux,
                     prior_mean_for_intercept = c(prior_mean_for_intercept),
                     prior_scale_for_intercept = c(prior_scale_for_intercept),
                     prior_df_for_intercept = c(prior_df_for_intercept),
@@ -228,12 +228,18 @@ stan_spatial.fit <- function(x, y, w,
                     prior_mean_rho = c(prior_mean_for_rho),
                     prior_scale_rho = c(prior_scale_for_rho),
                     prior_df_rho = c(prior_df_for_rho),
-                    prior_mean_sigma = c(prior_mean_for_sigma),
-                    prior_scale_sigma = c(prior_scale_for_sigma),
-                    prior_df_sigma = c(prior_df_for_sigma),
+                    prior_mean_for_aux = c(prior_mean_for_aux),
+                    prior_scale_for_aux = c(prior_scale_for_aux),
+                    prior_df_for_aux = c(prior_df_for_aux),
                     has_intercept = has_intercept,
-                    mod = mod)
-  standata$X <- array(standata$X, dim = c(standata$N, standata$K))
+                    mod = mod,
+                    global_prior_df,
+                    global_prior_df_for_intercept,
+                    global_prior_scale,
+                    global_prior_scale_for_intercept,
+                    num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0))
+  
+  standata$X <- array(standata$X, dim = c(standata$N, standata$K))  # FIX ME!!!
 
   # create scaling_factor a la Dan Simpson
   create_scaling_factor <- function(dat) {
@@ -261,25 +267,26 @@ stan_spatial.fit <- function(x, y, w,
   else
     standata$scaling_factor <- 0
 
-  pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "sigma",
+  pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "aux",
             "mean_PPD", "psi")
-
+browser()
   prior_info <- summarize_spatial_prior(
     user_prior = prior_stuff,
     user_prior_intercept = prior_intercept_stuff,
     user_prior_rho = prior_rho_stuff,
-    user_prior_sigma = prior_sigma_stuff,
+    user_prior_aux = if (has_aux == 1) {prior_aux_stuff} else {NULL},
     user_prior_tau = prior_tau_stuff,
     has_intercept = has_intercept,
     has_predictors = nvars > 0,
-    has_sigma = has_sigma,
+    has_aux = has_aux,
     has_rho = 1,
     has_tau = has_tau,
     adjusted_prior_scale = prior_scale,
     adjusted_prior_intercept_scale = prior_scale_for_intercept,
     adjusted_prior_scale_rho = prior_scale_for_rho,
-    adjusted_prior_scale_sigma = prior_scale_for_sigma,
-    adjusted_prior_scale_tau = prior_scale_for_tau)
+    adjusted_prior_aux_scale = prior_scale_for_aux,
+    adjusted_prior_scale_tau = prior_scale_for_tau,
+    family = family)
 
   stanfit <- stanmodels$spatial
   
@@ -338,7 +345,8 @@ stan_spatial.fit <- function(x, y, w,
     }
     new_names <- c(if (has_intercept) "(Intercept)", 
                    colnames(xtemp), "rho", if(mod == 2) c("tau"),
-                   if(family$family == "gaussian") "sigma", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
+                   if(family$family == "gaussian") "aux", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
+
     stanfit@sim$fnames_oi <- new_names
     return(structure(stanfit, prior.info = prior_info))
   }
@@ -348,19 +356,20 @@ stan_spatial.fit <- function(x, y, w,
 
 summarize_spatial_prior <- function(user_prior,
                                     user_prior_intercept,
-                                    user_prior_sigma,
+                                    user_prior_aux,
                                     user_prior_rho,
                                     user_prior_tau,
                                     has_intercept,
                                     has_predictors,
-                                    has_sigma,
+                                    has_aux,
                                     has_rho,
                                     has_tau,
                                     adjusted_prior_scale,
                                     adjusted_prior_intercept_scale,
-                                    adjusted_prior_scale_sigma,
                                     adjusted_prior_scale_rho,
-                                    adjusted_prior_scale_tau) {
+                                    adjusted_prior_scale_tau,
+                                    adjusted_prior_aux_scale,
+                                    family) {
   rescaled_coef <-
     user_prior$prior_autoscale && has_predictors &&
     !is.na(user_prior$prior_dist_name) &&
@@ -369,6 +378,11 @@ summarize_spatial_prior <- function(user_prior,
     user_prior_intercept$prior_autoscale_for_intercept && has_intercept &&
     !is.na(user_prior_intercept$prior_dist_name_for_intercept) &&
     (user_prior_intercept$prior_scale != adjusted_prior_intercept_scale)
+  if (has_aux) {
+    rescaled_aux <- user_prior_aux$prior_autoscale_for_aux &&
+      !is.na(user_prior_aux$prior_dist_name_for_aux) &&
+      (user_prior_aux$prior_scale_for_aux != adjusted_prior_aux_scale) 
+  }
   
   if (has_predictors && user_prior$prior_dist_name %in% "t") {
     if (all(user_prior$prior_df == 1)) {
@@ -385,12 +399,12 @@ summarize_spatial_prior <- function(user_prior,
       user_prior_intercept$prior_dist_name_for_intercept <- "student_t"
     }
   }
-  if (has_sigma &&
-      user_prior_sigma$prior_dist_name_for_sigma %in% "t") {
-    if (all(user_prior_sigma$prior_df_for_sigma == 1)) {
-      user_prior_sigma$prior_dist_name_for_sigma <- "cauchy"
+  if (has_aux &&
+      user_prior_aux$prior_dist_name_for_aux %in% "t") {
+    if (all(user_prior_aux$prior_df_for_aux == 1)) {
+      user_prior_aux$prior_dist_name_for_aux <- "cauchy"
     } else {
-      user_prior_sigma$prior_dist_name_for_sigma <- "student_t"
+      user_prior_aux$prior_dist_name_for_aux <- "student_t"
     }
   }
 
@@ -434,15 +448,15 @@ summarize_spatial_prior <- function(user_prior,
         df = if (prior_dist_name_for_intercept %in% "student_t")
           prior_df_for_intercept else NULL
       )),
-    prior_sigma = 
-      if (!has_sigma) NULL else with(user_prior_sigma, list(
-        dist = prior_dist_name_for_sigma,
-        location = prior_mean_for_sigma,
-        scale = prior_scale_for_sigma,
+    prior_aux = 
+      if (!has_aux) NULL else with(user_prior_aux, list(
+        dist = prior_dist_name_for_aux,
+        location = prior_mean_for_aux,
+        scale = prior_scale_for_aux,
         adjusted_scale = if (rescaled_int)
-          adjusted_prior_sigma_scale else NULL,
-        df = if (prior_dist_name_for_sigma %in% "student_t")
-          prior_df_for_sigma else NULL
+          adjusted_prior_aux_scale else NULL,
+        df = if (prior_dist_name_for_aux %in% "student_t")
+          prior_df_for_aux else NULL
       )),
     prior_rho = 
       if (!has_rho) NULL else with(user_prior_rho, list(
@@ -467,5 +481,25 @@ summarize_spatial_prior <- function(user_prior,
           prior_df_for_tau else NULL
       ))
   )
+  aux_name <- .rename_aux(family)
+  prior_list$prior_aux <- if (is.na(aux_name)) 
+    NULL else with(user_prior_aux, list(
+      dist = prior_dist_name_for_aux,
+      location = if (!is.na(prior_dist_name_for_aux) && 
+                     prior_dist_name_for_aux != "exponential")
+        prior_mean_for_aux else NULL,
+      scale = if (!is.na(prior_dist_name_for_aux) && 
+                  prior_dist_name_for_aux != "exponential")
+        prior_scale_for_aux else NULL,
+      adjusted_scale = if (rescaled_aux)
+        adjusted_prior_aux_scale else NULL,
+      df = if (!is.na(prior_dist_name_for_aux) && 
+               prior_dist_name_for_aux %in% "student_t")
+        prior_df_for_aux else NULL, 
+      rate = if (!is.na(prior_dist_name_for_aux) && 
+                 prior_dist_name_for_aux %in% "exponential")
+        1 / prior_scale_for_aux else NULL,
+      aux_name = aux_name
+    ))
   return(prior_list)
 }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index e645ea239..acfbf93a1 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -5,6 +5,7 @@ functions {
   #include "count_likelihoods.stan"
   // add negative_binomial likelihood
   // add gamma likelihood
+  #include "common_functions.stan"
 }
 data {
   int<lower=0> N;                 // number of regions
@@ -27,36 +28,56 @@ data {
   int<lower=0> prior_dist;
   int<lower=0> prior_dist_tau;
   int<lower=0> prior_dist_rho;
-  int<lower=0> prior_dist_sigma;
   real prior_mean_for_intercept;
   real<lower=0> prior_scale_for_intercept;
   real<lower=0> prior_df_for_intercept;
   vector[K] prior_mean;
   vector<lower=0>[K] prior_scale;
-  real<lower=0> prior_df[K];
+  vector<lower=0>[K] prior_df;
   real prior_mean_tau;
   real<lower=0> prior_scale_tau;
   real<lower=0> prior_df_tau;
   real prior_mean_rho;
   real<lower=0> prior_scale_rho;
   real<lower=0> prior_df_rho;
-  real prior_mean_sigma;
-  real<lower=0> prior_scale_sigma;
-  real<lower=0> prior_df_sigma;
+  real<lower=0> global_prior_df;    // for hs priors only
+  real<lower=0> global_prior_scale; // for hs priors only
+  int<lower=2> num_normals[prior_dist == 7 ? K : 0];
+  int<lower=0,upper=3> prior_dist_for_aux;
+  real<lower=0> prior_mean_for_aux;
+  real<lower=0> prior_scale_for_aux;
+  real<lower=0> prior_df_for_aux;
 }
 transformed data {
   real poisson_max = 30 * log(2);
+  int<lower=0> hs;
+  int<lower=0,upper=1> is_continuous;
+  if (prior_dist <= 2) hs = 0;
+  else if (prior_dist == 3) hs = 2;
+  else if (prior_dist == 4) hs = 4;
+  else hs = 0;
+  if (family == 1) is_continuous = 1;
+  else is_continuous = 0;
 }
 parameters {
   real gamma[has_intercept];  // raw intercept
-  vector[K] beta;             // predictors on covariates (including intercept)
+  vector[K] z_beta;
   vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
   real<lower=0,upper=(mod == 2? 1: positive_infinity())> rho;          // variance i.e. rho^2
   real<lower=0> tau[mod == 2? 1 : 0];        // variance i.e. tau^2
-  real<lower=0> sigma[family == 1? 1 : 0];  // applies only if family is gaussian
+  real<lower=0> global[hs];
+  vector<lower=0>[K] local[hs];
+  vector<lower=0>[K] mix[prior_dist == 5 || prior_dist == 6];
+  real<lower=0> aux_unscaled; # interpretation depends on family!
+  real<lower=0> one_over_lambda[prior_dist == 6];
 }
 transformed parameters {
+  vector[K] beta;             // predictors on covariates (including intercept)
+  // aux has to be defined first in the hs case
+  real aux = prior_dist_for_aux == 0 ? aux_unscaled : (prior_dist_for_aux <= 2 ?
+             prior_scale_for_aux * aux_unscaled + prior_mean_for_aux :
+             prior_scale_for_aux * aux_unscaled);
   vector[N] phi;          // non-centered random effect (spatial)
   vector[N] psi;
   phi[1:(N - 1)] = phi_raw;
@@ -65,7 +86,38 @@ transformed parameters {
     psi = phi * sqrt(inv(rho));
   else if (mod == 2)
     psi = tau[1]*(sqrt(1-rho)*theta_raw[1] + sqrt(rho/scaling_factor)*phi);
-    // psi = tau[1]*(sqrt(rho)*theta_raw[1] + sqrt(1-rho)*scaling_factor*phi);
+  // for regression coefficients
+  if      (prior_dist == 0) beta = z_beta;
+  else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
+  else if (prior_dist == 2) for (k in 1:K) {
+    beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
+  }
+  else if (prior_dist == 3) {
+    if (is_continuous == 1 && family == 1)
+      beta = hs_prior(z_beta, global, local, global_prior_scale, aux);
+    else beta = hs_prior(z_beta, global, local, global_prior_scale, 1);
+  }
+  else if (prior_dist == 4) {
+    if (is_continuous == 1 && family == 1)
+      beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux);
+    else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1);
+  }
+  else if (prior_dist == 5) // laplace
+    beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+  else if (prior_dist == 6) // lasso
+    beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+  else if (prior_dist == 7) { // product_normal
+    int z_pos = 1;
+    for (k in 1:K) {
+      beta[k] = z_beta[z_pos];
+      z_pos = z_pos + 1;
+      for (n in 2:num_normals[k]) {
+        beta[k] = beta[k] * z_beta[z_pos];
+        z_pos = z_pos + 1;
+      }
+      beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
+    }
+  }
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
@@ -77,7 +129,7 @@ model {
   // likelihoods
   if (family == 1) {
     eta = linkinv_gauss(eta, link);
-    target+= normal_lpdf(y_real | eta, sigma[1]);
+    target+= normal_lpdf(y_real | eta, aux);
   }
   else if (family == 2) {
     eta = linkinv_count(eta, link);
@@ -89,19 +141,52 @@ model {
   }
   // prior on spatial parameter vector
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
+  // non-centered parameterization on beta
+  target+= normal_lpdf(z_beta | 0, 1);
   // priors on coefficients
   if (has_intercept == 1) {
-    if (prior_dist_for_intercept == 1)
-      target+= normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
-    else if (prior_dist_for_intercept == 2)
-      target+= student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept, prior_scale_for_intercept);
-    /* else prior_dist_intercept is 0 and nothing is added */
+    if (prior_dist_for_intercept == 1)  // normal
+      target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
+    else if (prior_dist_for_intercept == 2)  // student_t
+      target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept,
+                               prior_scale_for_intercept);
+    /* else prior_dist is 0 and nothing is added */
   }
   if (K > 0) {
-    if (prior_dist == 1)
-      target+= normal_lpdf(beta | prior_mean, prior_scale);
-    else if (prior_dist == 2)
-      target+= student_t_lpdf(beta | prior_df, prior_mean, prior_scale);
+    // Log-priors for coefficients
+         if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
+    else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
+    else if (prior_dist == 3) { // hs
+      real log_half = -0.693147180559945286;
+      target += normal_lpdf(z_beta | 0, 1);
+      target += normal_lpdf(local[1] | 0, 1) - log_half;
+      target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+      target += normal_lpdf(global[1] | 0, 1) - log_half;
+      target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+    }
+    else if (prior_dist == 4) { // hs+
+      real log_half = -0.693147180559945286;
+      target += normal_lpdf(z_beta | 0, 1);
+      target += normal_lpdf(local[1] | 0, 1) - log_half;
+      target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+      target += normal_lpdf(local[3] | 0, 1) - log_half;
+      // unorthodox useage of prior_scale as another df hyperparameter
+      target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
+      target += normal_lpdf(global[1] | 0, 1) - log_half;
+      target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+    }
+    else if (prior_dist == 5) { // laplace
+      target += normal_lpdf(z_beta | 0, 1);
+      target += exponential_lpdf(mix[1] | 1);
+    }
+    else if (prior_dist == 6) { // lasso
+      target += normal_lpdf(z_beta | 0, 1);
+      target += exponential_lpdf(mix[1] | 1);
+      target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
+    }
+    else if (prior_dist == 7) { // product_normal
+      target += normal_lpdf(z_beta | 0, 1);
+    }
     /* else prior_dist is 0 and nothing is added */
   }
   if (mod == 2) { // BYM
@@ -122,12 +207,15 @@ model {
       target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
     /* else prior_dist_rho is 0 and nothing is added */
   }
-  if (family == 1) { // prior on sd if outcome is gaussian
-    if (prior_dist_sigma == 1)
-      target+= normal_lpdf(sigma[1] | prior_mean_sigma, prior_scale_sigma);
-    else if (prior_dist_sigma == 2)
-      target+= student_t_lpdf(sigma[1] | prior_df_sigma, prior_mean_sigma, prior_scale_sigma);
-    /* else prior_dist_sigma is 0 and nothing is added */
+  // priors on aux
+  if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
+    real log_half = -0.693147180559945286;
+    if (prior_dist_for_aux == 1)
+      target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
+    else if (prior_dist_for_aux == 2)
+      target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
+    else
+     target += exponential_lpdf(aux_unscaled | 1);
   }
 }
 generated quantities {
@@ -145,7 +233,7 @@ generated quantities {
     }
     if (family == 1) {
       eta = linkinv_gauss(eta, link);
-      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], sigma[1]);
+      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], aux);
     }
     else if (family == 2) {
       eta = linkinv_count(eta, link);

From fbdeeebcc8056cd1a1baaa2056e7de4822379391 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 9 Aug 2017 15:25:29 -0400
Subject: [PATCH 20/59] adding more likelihoods to CAR models

---
 R/stan_spatial.fit.R | 19 ++++++++++---------
 exec/spatial.stan    | 29 ++++++++++++++++++++++++++---
 2 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index d9b127051..57b8c16b6 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -40,7 +40,7 @@ stan_spatial.fit <- function(x, y, w,
   algorithm <- match.arg(algorithm)
 
   family <- validate_family(family)
-  supported_families <- c("binomial", "gaussian", "poisson")
+  supported_families <- c("binomial", "gaussian", "poisson", "Gamma", "neg_binomial_2")
   fam <- which(pmatch(supported_families, family$family, nomatch = 0L) == 1L)
   if (!length(fam)) 
     stop("'family' must be one of ", paste(supported_families, collapse = ", "))
@@ -50,27 +50,28 @@ stan_spatial.fit <- function(x, y, w,
   if (!length(link)) 
     stop("'link' must be one of ", paste(supported_links, collapse = ", "))
   
-  if (is.null(trials))
-    trials <- rep(0,length(y))
+  family_num <- switch(family$family,
+                       gaussian = 1,
+                       poisson = 2,
+                       neg_binomial_2 = 3,
+                       binomial = 4,
+                       Gamma = 5)
 
-  if (family$family == "gaussian") {
+  if (family$family %in% c("gaussian", "Gamma")) {
     y_real <- y
     y_int <- rep(0, length(y))
     trials <- rep(0, length(y))
-    family_num <- 1
   }
   else {
     y_real <- rep(0, length(y))
     y_int <- y
     if (family$family == "binomial") {
-      family_num <- 3
       if (is.null(trials) | any(y > trials))
         stop("Outcome values must be less than or equal to the corresponding value in `trials`.")
         
     }
-    else {  # poisson
+    else {  # poisson 
       trials <- rep(0, length(y))
-      family_num <- 2
     }
     if(!is.integer(y_int))
       stop("Outcome must be an integer for count likelihoods.")
@@ -269,7 +270,7 @@ stan_spatial.fit <- function(x, y, w,
 
   pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "aux",
             "mean_PPD", "psi")
-browser()
+
   prior_info <- summarize_spatial_prior(
     user_prior = prior_stuff,
     user_prior_intercept = prior_intercept_stuff,
diff --git a/exec/spatial.stan b/exec/spatial.stan
index acfbf93a1..31155d08e 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -14,8 +14,8 @@ data {
   vector[K] xbar;
   int<lower=0> trials[N];         // binomial trials (0 1d array if not applicable)
   int y_int[N];                   // outcome
-  real y_real[N];                 // outcome
-  int<lower=1,upper=3> family;    // family (1 = Gaussian, 2 = Poisson, 3 = Binomial)
+  vector[N] y_real;                 // outcome
+  int<lower=1,upper=5> family;    // family (1 = Gaussian, 2 = Poisson, 3 = Binomial)
   int link;
   int E_n;                        // number of adjacency pairs
   int edges[E_n, 2];              // adjacency pairs
@@ -52,6 +52,7 @@ transformed data {
   real poisson_max = 30 * log(2);
   int<lower=0> hs;
   int<lower=0,upper=1> is_continuous;
+  real sum_log_y = family == 1 ? not_a_number() : sum(log(y_real));
   if (prior_dist <= 2) hs = 0;
   else if (prior_dist == 3) hs = 2;
   else if (prior_dist == 4) hs = 4;
@@ -62,7 +63,7 @@ transformed data {
 parameters {
   real gamma[has_intercept];  // raw intercept
   vector[K] z_beta;
-  vector[N] theta_raw[mod == 2? 1 : 0];        // used for random effect (non-spatial)
+  vector[mod == 2? N : 0] theta_raw;        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
   real<lower=0,upper=(mod == 2? 1: positive_infinity())> rho;          // variance i.e. rho^2
   real<lower=0> tau[mod == 2? 1 : 0];        // variance i.e. tau^2
@@ -136,9 +137,16 @@ model {
     target+= poisson_log_lpmf(y_int | eta);
   }
   else if (family == 3) {
+    eta = linkinv_count(eta, link);
+    target += neg_binomial_2_lpmf(y_int | eta, aux);
+  }
+  else if (family == 4) {
     eta = linkinv_binom(eta, link);
     target+= binomial_lpmf(y_int | trials, inv_logit(eta));
   }
+  else if (family == 5) {
+    target += GammaReg(y_real, eta, aux, link, sum_log_y);
+  }
   // prior on spatial parameter vector
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
   // non-centered parameterization on beta
@@ -245,9 +253,24 @@ generated quantities {
       }
     }
     else if (family == 3) {
+      eta = linkinv_count(eta, link);
+      for (n in 1:N) {
+          real gamma_temp;
+          if (is_inf(aux)) gamma_temp = eta[n];
+          else gamma_temp = gamma_rng(aux, aux / eta[n]);
+          if (gamma_temp < poisson_max)
+            mean_PPD = mean_PPD + poisson_rng(gamma_temp);
+          else mean_PPD = mean_PPD + normal_rng(gamma_temp, sqrt(gamma_temp));
+      }
+    }
+    else if (family == 4) {
       eta = linkinv_binom(eta, link);
       for (n in 1:N) mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
     }
+    else if (family == 5) {
+      if (link > 1) eta = linkinv_gamma(eta, link);
+      for (n in 1:N) mean_PPD = mean_PPD + gamma_rng(aux, aux / eta[n]);
+    }
   }
   mean_PPD = mean_PPD / N;
 }

From 11c132e2abb6dd79da1721bc6786d1424a057217 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 10 Aug 2017 01:29:30 -0400
Subject: [PATCH 21/59] refactor stan chunks for spatial.stan

---
 exec/continuous.stan             | 61 +++++++++-----------
 exec/spatial.stan                | 99 +++-----------------------------
 inst/chunks/priors.stan          | 45 +++++++++++++++
 inst/chunks/priors_aux.stan      | 10 ++++
 inst/chunks/priors_glm.stan      | 50 +---------------
 inst/chunks/tparameters.stan     | 31 ++++++++++
 inst/chunks/tparameters_glm.stan | 33 +----------
 7 files changed, 126 insertions(+), 203 deletions(-)
 create mode 100644 inst/chunks/priors.stan
 create mode 100644 inst/chunks/priors_aux.stan
 create mode 100644 inst/chunks/tparameters.stan

diff --git a/exec/continuous.stan b/exec/continuous.stan
index c2ec00c21..c4099ab02 100644
--- a/exec/continuous.stan
+++ b/exec/continuous.stan
@@ -6,7 +6,7 @@ functions {
   #include "common_functions.stan"
   #include "continuous_likelihoods.stan"
   #include "SSfunctions.stan"
-  
+
   /*
    * Calculate lower bound on intercept
    *
@@ -34,8 +34,8 @@ functions {
     if (family == 4 && link == 5) return 0;
     return positive_infinity();
   }
-  
-  /** 
+
+  /**
   * test function for csr_matrix_times_vector
   *
   * @param m Integer number of rows
@@ -46,11 +46,11 @@ functions {
   * @param b Vector that is multiplied from the left by the CSR matrix
   * @return A vector that is the product of the CSR matrix and b
   */
-  vector test_csr_matrix_times_vector(int m, int n, vector w, 
+  vector test_csr_matrix_times_vector(int m, int n, vector w,
                                       int[] v, int[] u, vector b) {
-    return csr_matrix_times_vector(m, n, w, v, u, b); 
+    return csr_matrix_times_vector(m, n, w, v, u, b);
   }
-  
+
 }
 data {
   #include "NKX.stan"      // declares N, K, X, xbar, dense_X, nnz_x, w_x, v_x, u_x
@@ -63,7 +63,7 @@ data {
   // declares prior_{mean, scale, df}, prior_{mean, scale, df}_for_intercept, prior_{mean, scale, df}_for_aux
   #include "hyperparameters.stan"
   // declares t, p[t], l[t], q, len_theta_L, shape, scale, {len_}concentration, {len_}regularization
-  #include "glmer_stuff.stan"  
+  #include "glmer_stuff.stan"
   #include "glmer_stuff2.stan" // declares num_not_zero, w, v, u
   #include "data_betareg.stan"
   int<lower=0,upper=10> SSfun; // nonlinear function indicator, 0 for identity
@@ -93,10 +93,10 @@ parameters {
 }
 transformed parameters {
   // aux has to be defined first in the hs case
-  real aux = prior_dist_for_aux == 0 ? aux_unscaled : (prior_dist_for_aux <= 2 ? 
+  real aux = prior_dist_for_aux == 0 ? aux_unscaled : (prior_dist_for_aux <= 2 ?
              prior_scale_for_aux * aux_unscaled + prior_mean_for_aux :
              prior_scale_for_aux * aux_unscaled);
-  vector[z_dim] omega; // used in tparameters_betareg.stan             
+  vector[z_dim] omega; // used in tparameters_betareg.stan
   #include "tparameters_glm.stan" // defines beta, b, theta_L
   #include "tparameters_betareg.stan"
 
@@ -112,7 +112,7 @@ transformed parameters {
       }
     }
     else {
-      theta_L = make_theta_L(len_theta_L, p, 
+      theta_L = make_theta_L(len_theta_L, p,
                              aux, tau, scale, zeta, rho, z_T);
       b = make_b(z_b, theta_L, p, l);
     }
@@ -122,7 +122,7 @@ model {
   vector[N] eta_z; // beta regression linear predictor for phi
   #include "make_eta.stan" // defines eta
   if (t > 0) {
-    #include "eta_add_Zb.stan"    
+    #include "eta_add_Zb.stan"
   }
   if (has_intercept == 1) {
     if ((family == 1 || link == 2) || (family == 4 && link != 5)) eta = eta + gamma[1];
@@ -132,7 +132,7 @@ model {
   else {
     #include "eta_no_intercept.stan" // shifts eta
   }
-  
+
   if (SSfun > 0) { // nlmer
     matrix[len_y, K] P;
     P = reshape_vec(eta, len_y, K);
@@ -161,7 +161,7 @@ model {
     }
   }
   else if (has_weights == 0 && prior_PD == 0) { // unweighted log-likelihoods
-    #include "make_eta_z.stan"  // linear predictor in stan_betareg 
+    #include "make_eta_z.stan"  // linear predictor in stan_betareg
     // adjust eta_z according to links
     if (has_intercept_z == 1) {
       if (link_phi > 1) {
@@ -175,11 +175,11 @@ model {
       #include "eta_z_no_intercept.stan"
     }
     if (family == 1) {
-      if (link == 1) 
+      if (link == 1)
         target += normal_lpdf(y | eta, aux);
-      else if (link == 2) 
+      else if (link == 2)
         target += normal_lpdf(y | exp(eta), aux);
-      else 
+      else
         target += normal_lpdf(y | divide_real_by_vector(1, eta), aux);
       // divide_real_by_vector() is defined in common_functions.stan
     }
@@ -187,7 +187,7 @@ model {
       target += GammaReg(y, eta, aux, link, sum_log_y);
     }
     else if (family == 3) {
-      target += inv_gaussian(y, linkinv_inv_gaussian(eta, link), 
+      target += inv_gaussian(y, linkinv_inv_gaussian(eta, link),
                              aux, sum_log_y, sqrt_y);
     }
     else if (family == 4 && link_phi == 0) {
@@ -200,7 +200,7 @@ model {
       vector[N] mu_z;
       mu = linkinv_beta(eta, link);
       mu_z = linkinv_beta_z(eta_z, link_phi);
-      target += beta_lpdf(y | rows_dot_product(mu, mu_z), 
+      target += beta_lpdf(y | rows_dot_product(mu, mu_z),
                           rows_dot_product((1 - mu) , mu_z));
     }
   }
@@ -214,20 +214,11 @@ model {
     target += dot_product(weights, summands);
   }
 
-  // Log-priors
-  if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
-    real log_half = -0.693147180559945286;
-    if (prior_dist_for_aux == 1)
-      target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
-    else if (prior_dist_for_aux == 2)
-      target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
-    else 
-     target += exponential_lpdf(aux_unscaled | 1);
-  }
-    
+  #include "priors_aux.stan"
+
   #include "priors_glm.stan" // increments target()
   #include "priors_betareg.stan"
-  if (t > 0) decov_lp(z_b, z_T, rho, zeta, tau, 
+  if (t > 0) decov_lp(z_b, z_T, rho, zeta, tau,
                       regularization, delta, shape, t, p);
 }
 generated quantities {
@@ -238,8 +229,8 @@ generated quantities {
     if (dense_X) alpha[1] = gamma[1] - dot_product(xbar, beta);
     else alpha[1] = gamma[1];
   }
-  if (has_intercept_z == 1) { 
-    omega_int[1] = gamma_z[1] - dot_product(zbar, omega);  // adjust betareg intercept 
+  if (has_intercept_z == 1) {
+    omega_int[1] = gamma_z[1] - dot_product(zbar, omega);  // adjust betareg intercept
   }
   {
     vector[N] eta_z;
@@ -280,10 +271,10 @@ generated quantities {
     else { // has_intercept_z == 0
       #include "eta_z_no_intercept.stan"
     }
-    
+
     if (SSfun > 0) { // nlmer
       vector[len_y] eta_nlmer;
-      matrix[len_y, K] P;      
+      matrix[len_y, K] P;
       P = reshape_vec(eta, len_y, K);
       if (SSfun < 5) {
         if (SSfun <= 2) {
@@ -319,7 +310,7 @@ generated quantities {
       if (link > 1) eta = linkinv_inv_gaussian(eta, link);
       for (n in 1:len_y) mean_PPD = mean_PPD + inv_gaussian_rng(eta[n], aux);
     }
-    else if (family == 4 && link_phi == 0) { 
+    else if (family == 4 && link_phi == 0) {
       eta = linkinv_beta(eta, link);
       for (n in 1:N) {
         real eta_n = eta[n];
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 31155d08e..9362e4c06 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -1,10 +1,10 @@
+#include "Columbia_copyright.stan"
+#include "license.stan" // GPL3+
 // CAR SPATIAL MODELS
 functions {
   #include "continuous_likelihoods.stan"
   #include "binomial_likelihoods.stan"
   #include "count_likelihoods.stan"
-  // add negative_binomial likelihood
-  // add gamma likelihood
   #include "common_functions.stan"
 }
 data {
@@ -88,37 +88,7 @@ transformed parameters {
   else if (mod == 2)
     psi = tau[1]*(sqrt(1-rho)*theta_raw[1] + sqrt(rho/scaling_factor)*phi);
   // for regression coefficients
-  if      (prior_dist == 0) beta = z_beta;
-  else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
-  else if (prior_dist == 2) for (k in 1:K) {
-    beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
-  }
-  else if (prior_dist == 3) {
-    if (is_continuous == 1 && family == 1)
-      beta = hs_prior(z_beta, global, local, global_prior_scale, aux);
-    else beta = hs_prior(z_beta, global, local, global_prior_scale, 1);
-  }
-  else if (prior_dist == 4) {
-    if (is_continuous == 1 && family == 1)
-      beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux);
-    else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1);
-  }
-  else if (prior_dist == 5) // laplace
-    beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-  else if (prior_dist == 6) // lasso
-    beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-  else if (prior_dist == 7) { // product_normal
-    int z_pos = 1;
-    for (k in 1:K) {
-      beta[k] = z_beta[z_pos];
-      z_pos = z_pos + 1;
-      for (n in 2:num_normals[k]) {
-        beta[k] = beta[k] * z_beta[z_pos];
-        z_pos = z_pos + 1;
-      }
-      beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
-    }
-  }
+  #include "tparameters.stan"
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
@@ -134,7 +104,7 @@ model {
   }
   else if (family == 2) {
     eta = linkinv_count(eta, link);
-    target+= poisson_log_lpmf(y_int | eta);
+    target+= poisson_lpmf(y_int | eta);
   }
   else if (family == 3) {
     eta = linkinv_count(eta, link);
@@ -147,56 +117,13 @@ model {
   else if (family == 5) {
     target += GammaReg(y_real, eta, aux, link, sum_log_y);
   }
-  // prior on spatial parameter vector
+  // prior on spatial parameter vector (GMRF)
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
   // non-centered parameterization on beta
   target+= normal_lpdf(z_beta | 0, 1);
   // priors on coefficients
-  if (has_intercept == 1) {
-    if (prior_dist_for_intercept == 1)  // normal
-      target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
-    else if (prior_dist_for_intercept == 2)  // student_t
-      target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept,
-                               prior_scale_for_intercept);
-    /* else prior_dist is 0 and nothing is added */
-  }
-  if (K > 0) {
-    // Log-priors for coefficients
-         if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
-    else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
-    else if (prior_dist == 3) { // hs
-      real log_half = -0.693147180559945286;
-      target += normal_lpdf(z_beta | 0, 1);
-      target += normal_lpdf(local[1] | 0, 1) - log_half;
-      target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-      target += normal_lpdf(global[1] | 0, 1) - log_half;
-      target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-    }
-    else if (prior_dist == 4) { // hs+
-      real log_half = -0.693147180559945286;
-      target += normal_lpdf(z_beta | 0, 1);
-      target += normal_lpdf(local[1] | 0, 1) - log_half;
-      target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-      target += normal_lpdf(local[3] | 0, 1) - log_half;
-      // unorthodox useage of prior_scale as another df hyperparameter
-      target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
-      target += normal_lpdf(global[1] | 0, 1) - log_half;
-      target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-    }
-    else if (prior_dist == 5) { // laplace
-      target += normal_lpdf(z_beta | 0, 1);
-      target += exponential_lpdf(mix[1] | 1);
-    }
-    else if (prior_dist == 6) { // lasso
-      target += normal_lpdf(z_beta | 0, 1);
-      target += exponential_lpdf(mix[1] | 1);
-      target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
-    }
-    else if (prior_dist == 7) { // product_normal
-      target += normal_lpdf(z_beta | 0, 1);
-    }
-    /* else prior_dist is 0 and nothing is added */
-  }
+  #include "priors.stan"
+  // model specific priors
   if (mod == 2) { // BYM
     target+= normal_lpdf(theta_raw[1] | 0, 1);  // unstructured (random) effect
     if (prior_dist_rho == 1)
@@ -216,15 +143,7 @@ model {
     /* else prior_dist_rho is 0 and nothing is added */
   }
   // priors on aux
-  if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
-    real log_half = -0.693147180559945286;
-    if (prior_dist_for_aux == 1)
-      target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
-    else if (prior_dist_for_aux == 2)
-      target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
-    else
-     target += exponential_lpdf(aux_unscaled | 1);
-  }
+  #include "priors_aux.stan"
 }
 generated quantities {
   real mean_PPD = 0;
@@ -247,7 +166,7 @@ generated quantities {
       eta = linkinv_count(eta, link);
       for (n in 1:N) {
         if (eta[n] < poisson_max)
-          mean_PPD = mean_PPD + poisson_log_rng(eta[n]);
+          mean_PPD = mean_PPD + poisson_rng(eta[n]);
         else
           mean_PPD = mean_PPD + normal_rng(eta[n], sqrt(eta[n]));
       }
diff --git a/inst/chunks/priors.stan b/inst/chunks/priors.stan
new file mode 100644
index 000000000..ac31d3519
--- /dev/null
+++ b/inst/chunks/priors.stan
@@ -0,0 +1,45 @@
+// Log-priors for coefficients
+     if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
+else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
+else if (prior_dist == 3) { // hs
+  real log_half = -0.693147180559945286;
+  target += normal_lpdf(z_beta | 0, 1);
+  target += normal_lpdf(local[1] | 0, 1) - log_half;
+  target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+  target += normal_lpdf(global[1] | 0, 1) - log_half;
+  target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+}
+else if (prior_dist == 4) { // hs+
+  real log_half = -0.693147180559945286;
+  target += normal_lpdf(z_beta | 0, 1);
+  target += normal_lpdf(local[1] | 0, 1) - log_half;
+  target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+  target += normal_lpdf(local[3] | 0, 1) - log_half;
+  // unorthodox useage of prior_scale as another df hyperparameter
+  target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
+  target += normal_lpdf(global[1] | 0, 1) - log_half;
+  target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+}
+else if (prior_dist == 5) { // laplace
+  target += normal_lpdf(z_beta | 0, 1);
+  target += exponential_lpdf(mix[1] | 1);
+}
+else if (prior_dist == 6) { // lasso
+  target += normal_lpdf(z_beta | 0, 1);
+  target += exponential_lpdf(mix[1] | 1);
+  target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
+}
+else if (prior_dist == 7) { // product_normal
+  target += normal_lpdf(z_beta | 0, 1);
+}
+/* else prior_dist is 0 and nothing is added */
+
+// Log-prior for intercept
+if (has_intercept == 1) {
+  if (prior_dist_for_intercept == 1)  // normal
+    target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
+  else if (prior_dist_for_intercept == 2)  // student_t
+    target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept,
+                             prior_scale_for_intercept);
+  /* else prior_dist is 0 and nothing is added */
+}
diff --git a/inst/chunks/priors_aux.stan b/inst/chunks/priors_aux.stan
new file mode 100644
index 000000000..986f5058c
--- /dev/null
+++ b/inst/chunks/priors_aux.stan
@@ -0,0 +1,10 @@
+// Log-priors
+if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
+  real log_half = -0.693147180559945286;
+  if (prior_dist_for_aux == 1)
+    target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
+  else if (prior_dist_for_aux == 2)
+    target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
+  else
+   target += exponential_lpdf(aux_unscaled | 1);
+}
diff --git a/inst/chunks/priors_glm.stan b/inst/chunks/priors_glm.stan
index 68647150f..946ba11eb 100644
--- a/inst/chunks/priors_glm.stan
+++ b/inst/chunks/priors_glm.stan
@@ -1,58 +1,14 @@
-  // Log-priors for coefficients
-       if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
-  else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
-  else if (prior_dist == 3) { // hs
-    real log_half = -0.693147180559945286;
-    target += normal_lpdf(z_beta | 0, 1);
-    target += normal_lpdf(local[1] | 0, 1) - log_half;
-    target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-    target += normal_lpdf(global[1] | 0, 1) - log_half;
-    target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-  }
-  else if (prior_dist == 4) { // hs+
-    real log_half = -0.693147180559945286;
-    target += normal_lpdf(z_beta | 0, 1);
-    target += normal_lpdf(local[1] | 0, 1) - log_half;
-    target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-    target += normal_lpdf(local[3] | 0, 1) - log_half;
-    // unorthodox useage of prior_scale as another df hyperparameter
-    target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
-    target += normal_lpdf(global[1] | 0, 1) - log_half;
-    target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-  }
-  else if (prior_dist == 5) { // laplace
-    target += normal_lpdf(z_beta | 0, 1);
-    target += exponential_lpdf(mix[1] | 1);
-  }
-  else if (prior_dist == 6) { // lasso
-    target += normal_lpdf(z_beta | 0, 1);
-    target += exponential_lpdf(mix[1] | 1);
-    target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
-  }
-  else if (prior_dist == 7) { // product_normal
-    target += normal_lpdf(z_beta | 0, 1);
-  }
-  /* else prior_dist is 0 and nothing is added */
+  #include "priors.stan"
   
-  // Log-prior for intercept  
-  if (has_intercept == 1) {
-    if (prior_dist_for_intercept == 1)  // normal
-      target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
-    else if (prior_dist_for_intercept == 2)  // student_t
-      target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept, 
-                               prior_scale_for_intercept);
-    /* else prior_dist is 0 and nothing is added */
-  }
-
   if (K_smooth) {
     target += normal_lpdf(z_beta_smooth | 0, 1);
     if (prior_dist_for_smooth > 0) {
       real log_half = -0.693147180559945286;
-      if (prior_dist_for_smooth == 1) 
+      if (prior_dist_for_smooth == 1)
         target += normal_lpdf(smooth_sd_raw | 0, 1) - log_half;
       else if (prior_dist_for_smooth == 2)
         target += student_t_lpdf(smooth_sd_raw | prior_df_for_smooth, 0, 1) - log_half;
-      else if (prior_dist_for_smooth == 3) 
+      else if (prior_dist_for_smooth == 3)
         target += exponential_lpdf(smooth_sd_raw | 1);
     }
   }
diff --git a/inst/chunks/tparameters.stan b/inst/chunks/tparameters.stan
new file mode 100644
index 000000000..e050733ad
--- /dev/null
+++ b/inst/chunks/tparameters.stan
@@ -0,0 +1,31 @@
+if      (prior_dist == 0) beta = z_beta;
+else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
+else if (prior_dist == 2) for (k in 1:K) {
+  beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
+}
+else if (prior_dist == 3) {
+  if (is_continuous == 1 && family == 1)
+    beta = hs_prior(z_beta, global, local, global_prior_scale, aux);
+  else beta = hs_prior(z_beta, global, local, global_prior_scale, 1);
+}
+else if (prior_dist == 4) {
+  if (is_continuous == 1 && family == 1)
+    beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux);
+  else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1);
+}
+else if (prior_dist == 5) // laplace
+  beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+else if (prior_dist == 6) // lasso
+  beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+else if (prior_dist == 7) { // product_normal
+  int z_pos = 1;
+  for (k in 1:K) {
+    beta[k] = z_beta[z_pos];
+    z_pos = z_pos + 1;
+    for (n in 2:num_normals[k]) {
+      beta[k] = beta[k] * z_beta[z_pos];
+      z_pos = z_pos + 1;
+    }
+    beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
+  }
+}
diff --git a/inst/chunks/tparameters_glm.stan b/inst/chunks/tparameters_glm.stan
index b3836f6a2..a7a63640f 100644
--- a/inst/chunks/tparameters_glm.stan
+++ b/inst/chunks/tparameters_glm.stan
@@ -3,37 +3,8 @@
   vector[K_smooth > 0 ? smooth_map[K_smooth] : 0] smooth_sd;
   vector[q] b;
   vector[len_theta_L] theta_L;
-  if      (prior_dist == 0) beta = z_beta;
-  else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
-  else if (prior_dist == 2) for (k in 1:K) {
-    beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
-  }
-  else if (prior_dist == 3) {
-    if (is_continuous == 1 && family == 1)
-      beta = hs_prior(z_beta, global, local, global_prior_scale, aux);
-    else beta = hs_prior(z_beta, global, local, global_prior_scale, 1);
-  }
-  else if (prior_dist == 4) {
-    if (is_continuous == 1 && family == 1)
-      beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux);
-    else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1);
-  }
-  else if (prior_dist == 5) // laplace
-    beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-  else if (prior_dist == 6) // lasso
-    beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-  else if (prior_dist == 7) { // product_normal
-    int z_pos = 1;
-    for (k in 1:K) {
-      beta[k] = z_beta[z_pos];
-      z_pos = z_pos + 1;
-      for (n in 2:num_normals[k]) {
-        beta[k] = beta[k] * z_beta[z_pos];
-        z_pos = z_pos + 1;
-      }
-      beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
-    }
-  }
+
+  #include "tparameters.stan"
 
   if (K_smooth) {
     smooth_sd = prior_mean_for_smooth + prior_scale_for_smooth .* smooth_sd_raw;

From aeb898b22928d925045dc091c44c6439aa4a2475 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Fri, 11 Aug 2017 01:43:47 -0400
Subject: [PATCH 22/59] draft of CAR vignette; fixing binomial lpmf bug in
 spatial.stan

---
 R/datasets.R          |  24 ++---
 R/loo.R               |   1 +
 R/stan_besag.R        |  11 ++-
 R/stan_bym.R          |   5 +-
 R/stan_spatial.fit.R  | 137 +++++++++++++---------------
 data/lattice.rda      | Bin 0 -> 33425 bytes
 data/lattice10.rda    | Bin 3755 -> 0 bytes
 exec/spatial.stan     |  80 ++++++++--------
 vignettes/spatial.Rmd | 208 ++++++++++++++++++++++++++++++++++++++++++
 9 files changed, 328 insertions(+), 138 deletions(-)
 create mode 100644 data/lattice.rda
 delete mode 100644 data/lattice10.rda
 create mode 100644 vignettes/spatial.Rmd

diff --git a/R/datasets.R b/R/datasets.R
index 58e1aa8d2..f5309986c 100644
--- a/R/datasets.R
+++ b/R/datasets.R
@@ -20,7 +20,7 @@
 #' Small datasets for use in \pkg{rstanarm} examples and vignettes.
 #'
 #' @name rstanarm-datasets
-#' @aliases kidiq roaches wells bball1970 bball2006 mortality tumors radon lattice10
+#' @aliases kidiq roaches wells bball1970 bball2006 mortality tumors radon lattice
 #' @format 
 #' \describe{
 #' \item{\code{bball1970}}{
@@ -139,19 +139,21 @@
 #' \item \code{educ} Years of education (head of household)
 #' }
 #' }
-#' \item{\code{lattice10}}{
-#' The \code{grid_sim} SpatialPolygonsDataFrame object is an artificial lattice
-#' that has been used to simulate spatial dependence as a Gaussian Markov Random
-#' Field (GMRF).
+#' \item{\code{lattice}}{ The \code{grid_sim10} and \code{grid_sim30}
+#' SpatialPolygonsDataFrame objects are a simulated lattice of 100 and 900
+#' spatial units, respectively. These objects contain spatially dependent
+#' observations simulated as a Gaussian Markov Random Field (GMRF).
 #' 
-#' Souce: Simulated data
+#' Source: Simulated data
 #' 
-#' 100 obs. of 3 variables
 #' \itemize{
-#' \item \code{gmrf} GMRF data simulated using Cholesky decomposition sampling from
-#' the precision form of the Multivariate Normal distribution centered at zero.
-#' \item \code{r} X-position of a location on the grid (origin is at the bottom-left).
-#' \item \code{c} Y-position of a location on the grid (origin is at the bottom-left).
+#' \item \code{gmrf} GMRF data simulated using Cholesky decomposition sampling
+#' from the precision form of the Multivariate Normal distribution centered at
+#' zero.
+#' \item \code{r} X-position of a location on the grid (origin is at the
+#' bottom-left).
+#' \item \code{c} Y-position of a location on the grid (origin is at the
+#' bottom-left).
 #' }
 #' }
 #' }
diff --git a/R/loo.R b/R/loo.R
index dc4f6ac17..708f5f9bf 100644
--- a/R/loo.R
+++ b/R/loo.R
@@ -140,6 +140,7 @@ loo.stanreg <- function(x, ..., k_threshold = NULL) {
   } else {
     k_threshold <- 0.7
   }
+
   loo_x <- suppressWarnings(loo.function(ll_fun(x), args = ll_args(x), ...))
   
   bad_obs <- loo::pareto_k_ids(loo_x, k_threshold)
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 5d36a9b9f..5f15a3d08 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -42,7 +42,7 @@
 #' @param trials If \code{family = binomial()} then a vector of trials (equal
 #' in length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_rho The prior distribution on the variance of the non-centered structured (spatial) effect.
+#' @param prior_tau The prior distribution on the variance of the non-centered structured (spatial) effect.
 #' 
 #' @details The \code{stan_besag} model is similar to the analogous model in
 #'   R-INLA. However, instead of using the integrated Laplace approximation
@@ -61,9 +61,10 @@
 #' @examples 
 #' ### Simulated Data on a Lattice
 #' 
-#' data("lattice10", package = "rstanarm")
+#' data("lattice", package = "rstanarm")
 #' 
 #' # plot GMRF
+#' grid_sim <- grid_sim10
 #' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
 #' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
 #'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
@@ -87,13 +88,13 @@
 #' pp_check(fit_besag)
 #'
 stan_besag <- function(formula,
-                     family = gaussian(),
+                     family = NULL,
                      data,
                      trials = NULL,
                      W,
                      ...,
                      prior = normal(), prior_intercept = normal(),
-                     prior_aux = NULL, prior_rho = normal(),
+                     prior_tau = normal(), prior_aux = NULL,
                      prior_PD = FALSE,
                      algorithm = c("sampling", "meanfield", "fullrank"),
                      adapt_delta = NULL,
@@ -117,7 +118,7 @@ stan_besag <- function(formula,
                               prior = prior,
                               prior_intercept = prior_intercept,
                               prior_aux = prior_aux,
-                              prior_rho = prior_rho,
+                              prior_tau = prior_tau,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 546ca03a2..03372be62 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -71,9 +71,10 @@
 #' @examples 
 #' ### Simulated Data on a Lattice
 #' 
-#' data("lattice10", package = "rstanarm")
+#' data("lattice", package = "rstanarm")
 #' 
 #' # plot GMRF
+#' grid_sim <- grid_sim10
 #' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
 #' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
 #'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
@@ -102,7 +103,7 @@ stan_bym <- function(formula,
                         W,
                         ...,
                         prior = normal(), prior_intercept = normal(),
-                        prior_aux = NULL, prior_rho = beta(0.5,0.5), prior_tau = normal(),
+                        prior_tau = normal(), prior_rho = beta(0.5,0.5), prior_aux = NULL,
                         prior_PD = FALSE,
                         algorithm = c("sampling", "meanfield", "fullrank"),
                         adapt_delta = NULL,
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 57b8c16b6..90886f9ed 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -25,12 +25,12 @@
 #' 
 
 stan_spatial.fit <- function(x, y, w,
-                             trials = NULL,
-                             family = gaussian(),
                              stan_function = c("stan_besag", "stan_bym"),
+                             family = NULL,
+                             trials = NULL,
                              ...,
                              prior = normal(), prior_intercept = normal(),
-                             prior_aux = NULL, prior_rho = NULL, prior_tau = NULL,
+                             prior_tau = normal(), prior_aux = NULL, prior_rho = NULL,
                              prior_PD = FALSE,
                              algorithm = c("sampling", "meanfield", "fullrank"),
                              adapt_delta = NULL,
@@ -56,34 +56,37 @@ stan_spatial.fit <- function(x, y, w,
                        neg_binomial_2 = 3,
                        binomial = 4,
                        Gamma = 5)
-
+  
   if (family$family %in% c("gaussian", "Gamma")) {
+    is_continuous <- TRUE
     y_real <- y
-    y_int <- rep(0, length(y))
-    trials <- rep(0, length(y))
+    y_int <- array(0, dim = c(0))
+    trials <- array(0, dim = c(0))
   }
   else {
-    y_real <- rep(0, length(y))
+    is_continuous <- FALSE
+    y_real <- array(0, dim = c(0))
     y_int <- y
-    if (family$family == "binomial") {
-      if (is.null(trials) | any(y > trials))
-        stop("Outcome values must be less than or equal to the corresponding value in `trials`.")
-        
-    }
-    else {  # poisson 
-      trials <- rep(0, length(y))
-    }
+  }
+  
+  if (family$family %in% c("binomial", "poisson"))
+    has_aux <- FALSE
+  else
+    has_aux <- TRUE
+  
+  if (family$family %in% c("binomial", "poisson", "neg_binomial_2")) {
     if(!is.integer(y_int))
       stop("Outcome must be an integer for count likelihoods.")
+    if (family$family == "binomial" & (is.null(trials) | any(y > trials)))
+      stop("Outcome values must be less than or equal to the corresponding value in `trials`.")
   }
   
   if (stan_function == "stan_besag")
-    mod <- 1
+    model_type <- 1
   else if (stan_function == "stan_bym")
-    mod <- 2
+    model_type <- 2
   
-  sparse <- FALSE
-  x_stuff <- center_x(x, sparse)
+  x_stuff <- center_x(x, sparse = FALSE)
   for (i in names(x_stuff)) # xtemp, xbar, has_intercept
     assign(i, x_stuff[[i]])
   nvars <- ncol(xtemp)
@@ -92,12 +95,6 @@ stan_spatial.fit <- function(x, y, w,
                     "laplace", "lasso", "product_normal")
   ok_intercept_dists <- ok_dists
   ok_scale_dists <- nlist("normal", student_t = "t", "cauchy", "exponential")
-
-  # Deal with prior
-  prior_stuff <- handle_glm_prior(prior, nvars, family$link, default_scale = 2.5, 
-                                  ok_dists = ok_dists)
-  for (i in names(prior_stuff)) # prior_{dist, mean, scale, df, autoscale}
-    assign(i, prior_stuff[[i]])
   
   # Deal with prior_intercept
   prior_intercept_stuff <- handle_glm_prior(prior_intercept, nvars = 1, 
@@ -107,58 +104,51 @@ stan_spatial.fit <- function(x, y, w,
                                          "_for_intercept")
   for (i in names(prior_intercept_stuff)) # prior_{dist, mean, scale, df, autoscale}_for_intercept
     assign(i, prior_intercept_stuff[[i]])
+
+  # Deal with prior
+  prior_stuff <- handle_glm_prior(prior, nvars, family$link, default_scale = 2.5, 
+                                  ok_dists = ok_dists)
+  for (i in names(prior_stuff)) # prior_{dist, mean, scale, df, autoscale}
+    assign(i, prior_stuff[[i]])
   
-  # Deal with prior_rho and prior_sigma
+  # Deal with prior_tau
+  prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, family$link, default_scale = 1, 
+                                      ok_dists = ok_scale_dists)
+  names(prior_tau_stuff) <- paste0(names(prior_tau_stuff), 
+                                   "_for_tau")
+  for (i in names(prior_tau_stuff)) # prior_{dist, mean, scale, df, autoscale}
+    assign(i, prior_tau_stuff[[i]])
+  
+  # Deal with prior_rho
   if (stan_function == "stan_bym") {
-    has_tau <- 1
-    prior_tau_stuff <- handle_glm_prior(prior_tau, nvars = 1, family$link, default_scale = 1, 
-                                          ok_dists = ok_scale_dists)
-    names(prior_tau_stuff) <- paste0(names(prior_tau_stuff), 
-                                           "_for_tau")
-    for (i in names(prior_tau_stuff)) # prior_{dist, mean, scale, df, autoscale}
-      assign(i, prior_tau_stuff[[i]])
-    # In BYM rho is constrained so appropriate prior is Beta dist.
+    has_rho <- 1
     if (is.null(prior_rho)) {
       prior_dist_for_rho <- 0
-      prior_rho$alpha <- 1
-      prior_rho$beta <- 1
+      prior_rho$alpha <- 0
+      prior_rho$beta <- 0
       prior_dist_name_for_rho <- NA
     }
     else {
       prior_dist_for_rho <- 1
       prior_dist_name_for_rho <- "beta"
     }
-
-    prior_mean_for_rho <- 0
-    prior_scale_for_rho <- 1
-    prior_df_for_rho <- 1
     prior_rho_stuff <- list(prior_dist_name_for_rho = prior_dist_name_for_rho,
                             shape1 = prior_rho$alpha,
                             shape2 = prior_rho$beta,
-                            prior_mean_for_rho = prior_mean_for_rho,
-                            prior_scale_for_rho = prior_scale_for_rho,
-                            prior_df_for_rho = prior_df_for_rho
-                            )
+                            prior_mean_for_rho = 0,
+                            prior_scale_for_rho = 0,
+                            prior_df_for_rho = 0)
   }
   else if (stan_function == "stan_besag") {
-    has_tau <- 0
-    prior_rho_stuff <- handle_glm_prior(prior_rho, nvars = 1, family$link, default_scale = 1, 
-                                          ok_dists = ok_scale_dists)
-    names(prior_rho_stuff) <- paste0(names(prior_rho_stuff), 
-                                       "_for_rho")
-    for (i in names(prior_rho_stuff)) # prior_{dist, mean, scale, df, autoscale}
-      assign(i, prior_rho_stuff[[i]])
-
-    prior_rho_stuff$shape1 <- 1
-    prior_rho_stuff$shape2 <- 1
-    prior_dist_for_tau <- 0
-    prior_mean_for_tau <- 0
-    prior_scale_for_tau <- 1
-    prior_df_for_tau <- 1
+    has_rho <- 0
+    prior_dist_for_rho <- 0
+    prior_rho_stuff <- list(prior_dist_name_for_rho = NA)
+    prior_scale_for_rho <- 0
+    prior_rho_stuff$shape1 <- 0
+    prior_rho_stuff$shape2 <- 0
   }
 
-  if (family$family %in% c("gaussian","Gamma","neg_binomial_2")) {
-    has_aux <- 1
+  if (has_aux) {
     prior_aux_stuff <- handle_glm_prior(prior_aux, nvars = 1, family$link, default_scale = 1, 
                                     ok_dists = ok_dists)
     names(prior_aux_stuff) <- paste0(names(prior_aux_stuff), 
@@ -167,7 +157,6 @@ stan_spatial.fit <- function(x, y, w,
       assign(i, prior_aux_stuff[[i]])
   }
   else {
-    has_aux <- 0
     prior_dist_for_aux <- 0
     prior_mean_for_aux <- 0
     prior_scale_for_aux <- 1
@@ -205,6 +194,8 @@ stan_spatial.fit <- function(x, y, w,
                     E_n = nrow(edges),
                     family = family_num,
                     link = link,
+                    is_continuous = is_continuous,
+                    has_aux = has_aux,
                     X = xtemp,
                     xbar = as.array(xbar),
                     y_real = y_real,
@@ -214,7 +205,6 @@ stan_spatial.fit <- function(x, y, w,
                     shape2_rho = c(prior_rho_stuff$shape2),
                     prior_dist_for_intercept = prior_dist_for_intercept,
                     prior_dist = prior_dist,
-                    prior_dist_rho = prior_dist_for_rho,
                     prior_dist_tau = prior_dist_for_tau,
                     prior_dist_for_aux = prior_dist_for_aux,
                     prior_mean_for_intercept = c(prior_mean_for_intercept),
@@ -226,21 +216,17 @@ stan_spatial.fit <- function(x, y, w,
                     prior_mean_tau = c(prior_mean_for_tau),
                     prior_scale_tau = c(prior_scale_for_tau),
                     prior_df_tau = c(prior_df_for_tau),
-                    prior_mean_rho = c(prior_mean_for_rho),
-                    prior_scale_rho = c(prior_scale_for_rho),
-                    prior_df_rho = c(prior_df_for_rho),
                     prior_mean_for_aux = c(prior_mean_for_aux),
                     prior_scale_for_aux = c(prior_scale_for_aux),
                     prior_df_for_aux = c(prior_df_for_aux),
                     has_intercept = has_intercept,
-                    mod = mod,
+                    model_type = model_type,
                     global_prior_df,
                     global_prior_df_for_intercept,
                     global_prior_scale,
                     global_prior_scale_for_intercept,
-                    num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0))
-  
-  standata$X <- array(standata$X, dim = c(standata$N, standata$K))  # FIX ME!!!
+                    num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0),
+                    prior_dist_rho = prior_dist_for_rho)
 
   # create scaling_factor a la Dan Simpson
   create_scaling_factor <- function(dat) {
@@ -268,7 +254,7 @@ stan_spatial.fit <- function(x, y, w,
   else
     standata$scaling_factor <- 0
 
-  pars <- c(if (has_intercept) "alpha", "beta", "rho", if(mod == 2) c("tau"), if(family$family == "gaussian") "aux",
+  pars <- c(if (has_intercept) "alpha", "beta", if(model_type == 2) "rho", "tau", if(has_aux) "aux",
             "mean_PPD", "psi")
 
   prior_info <- summarize_spatial_prior(
@@ -280,17 +266,16 @@ stan_spatial.fit <- function(x, y, w,
     has_intercept = has_intercept,
     has_predictors = nvars > 0,
     has_aux = has_aux,
-    has_rho = 1,
-    has_tau = has_tau,
+    has_rho = has_rho,
+    has_tau = 1,
     adjusted_prior_scale = prior_scale,
     adjusted_prior_intercept_scale = prior_scale_for_intercept,
-    adjusted_prior_scale_rho = prior_scale_for_rho,
     adjusted_prior_aux_scale = prior_scale_for_aux,
     adjusted_prior_scale_tau = prior_scale_for_tau,
     family = family)
 
   stanfit <- stanmodels$spatial
-  
+
   # n.b. optimizing is not supported
   if (algorithm == "optimizing") {
     out <-
@@ -345,8 +330,8 @@ stan_spatial.fit <- function(x, y, w,
       } 
     }
     new_names <- c(if (has_intercept) "(Intercept)", 
-                   colnames(xtemp), "rho", if(mod == 2) c("tau"),
-                   if(family$family == "gaussian") "aux", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
+                   colnames(xtemp), if(model_type == 2) "rho", "tau",
+                   if(has_aux) "aux", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
 
     stanfit@sim$fnames_oi <- new_names
     return(structure(stanfit, prior.info = prior_info))
diff --git a/data/lattice.rda b/data/lattice.rda
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diff --git a/data/lattice10.rda b/data/lattice10.rda
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diff --git a/exec/spatial.stan b/exec/spatial.stan
index 9362e4c06..f09c8cc11 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -11,23 +11,25 @@ data {
   int<lower=0> N;                 // number of regions
   int<lower=0> K;                 // number of predictors (inc intercept)
   matrix[N,K] X;                  // model matrix
+  int<lower=1,upper=5> family;
+  int<lower=0,upper=1> is_continuous;
+  int<lower=0,upper=1> has_aux;
+  int<lower=1,upper=2> model_type;       // Besag = 1; BYM = 2
+  int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
-  int<lower=0> trials[N];         // binomial trials (0 1d array if not applicable)
-  int y_int[N];                   // outcome
-  vector[N] y_real;                 // outcome
-  int<lower=1,upper=5> family;    // family (1 = Gaussian, 2 = Poisson, 3 = Binomial)
+  int<lower=0> trials[family == 4 ? N : 0];         // binomial trials (0 1d array if not applicable)
+  int y_int[is_continuous == 1 ? 0 : N];                   // outcome
+  vector[is_continuous == 1 ? N : 0] y_real;                 // outcome
   int link;
   int E_n;                        // number of adjacency pairs
   int edges[E_n, 2];              // adjacency pairs
+  int<lower=0,upper=1> prior_dist_rho;
   real<lower=0> shape1_rho;        // priors
   real<lower=0> shape2_rho;        // priors
-  int<lower=0,upper=1> has_intercept;
-  int<lower=1,upper=2> mod;       // 1 = besag (icar); 2 = bym
   real scaling_factor;
   int<lower=0> prior_dist_for_intercept;
   int<lower=0> prior_dist;
   int<lower=0> prior_dist_tau;
-  int<lower=0> prior_dist_rho;
   real prior_mean_for_intercept;
   real<lower=0> prior_scale_for_intercept;
   real<lower=0> prior_df_for_intercept;
@@ -37,9 +39,6 @@ data {
   real prior_mean_tau;
   real<lower=0> prior_scale_tau;
   real<lower=0> prior_df_tau;
-  real prior_mean_rho;
-  real<lower=0> prior_scale_rho;
-  real<lower=0> prior_df_rho;
   real<lower=0> global_prior_df;    // for hs priors only
   real<lower=0> global_prior_scale; // for hs priors only
   int<lower=2> num_normals[prior_dist == 7 ? K : 0];
@@ -49,44 +48,41 @@ data {
   real<lower=0> prior_df_for_aux;
 }
 transformed data {
-  real poisson_max = 30 * log(2);
+  real poisson_max = pow(2.0, 30.0);
   int<lower=0> hs;
-  int<lower=0,upper=1> is_continuous;
   real sum_log_y = family == 1 ? not_a_number() : sum(log(y_real));
   if (prior_dist <= 2) hs = 0;
   else if (prior_dist == 3) hs = 2;
   else if (prior_dist == 4) hs = 4;
   else hs = 0;
-  if (family == 1) is_continuous = 1;
-  else is_continuous = 0;
 }
 parameters {
   real gamma[has_intercept];  // raw intercept
-  vector[K] z_beta;
-  vector[mod == 2? N : 0] theta_raw;        // used for random effect (non-spatial)
+  vector[K] z_beta;  // standard normal term
+  vector[model_type == 2? N : 0] theta_raw;        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0,upper=(mod == 2? 1: positive_infinity())> rho;          // variance i.e. rho^2
-  real<lower=0> tau[mod == 2? 1 : 0];        // variance i.e. tau^2
+  real<lower=0,upper=1> rho[model_type == 2];          // variance i.e. rho^2
+  real<lower=0> tau;        // variance i.e. tau^2
   real<lower=0> global[hs];
   vector<lower=0>[K] local[hs];
   vector<lower=0>[K] mix[prior_dist == 5 || prior_dist == 6];
-  real<lower=0> aux_unscaled; # interpretation depends on family!
+  real<lower=0> aux_unscaled[has_aux]; # interpretation depends on family!
   real<lower=0> one_over_lambda[prior_dist == 6];
 }
 transformed parameters {
   vector[K] beta;             // predictors on covariates (including intercept)
   // aux has to be defined first in the hs case
-  real aux = prior_dist_for_aux == 0 ? aux_unscaled : (prior_dist_for_aux <= 2 ?
-             prior_scale_for_aux * aux_unscaled + prior_mean_for_aux :
-             prior_scale_for_aux * aux_unscaled);
+  real aux = has_aux == 0 ? 0 : (prior_dist_for_aux == 0 ? aux_unscaled[1] : (prior_dist_for_aux <= 2 ?
+             prior_scale_for_aux * aux_unscaled[1] + prior_mean_for_aux :
+             prior_scale_for_aux * aux_unscaled[1]));
   vector[N] phi;          // non-centered random effect (spatial)
   vector[N] psi;
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
-  if (mod == 1)
-    psi = phi * sqrt(inv(rho));
-  else if (mod == 2)
-    psi = tau[1]*(sqrt(1-rho)*theta_raw[1] + sqrt(rho/scaling_factor)*phi);
+  if (model_type == 1)
+    psi = phi * sqrt(inv(tau));
+  else if (model_type == 2)
+    psi = tau*(sqrt(1-rho[1])*theta_raw[1] + sqrt(rho[1]/scaling_factor)*phi);
   // for regression coefficients
   #include "tparameters.stan"
 }
@@ -112,38 +108,34 @@ model {
   }
   else if (family == 4) {
     eta = linkinv_binom(eta, link);
-    target+= binomial_lpmf(y_int | trials, inv_logit(eta));
+    target+= binomial_lpmf(y_int | trials, eta);
   }
   else if (family == 5) {
     target += GammaReg(y_real, eta, aux, link, sum_log_y);
   }
   // prior on spatial parameter vector (GMRF)
   target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
-  // non-centered parameterization on beta
-  target+= normal_lpdf(z_beta | 0, 1);
   // priors on coefficients
   #include "priors.stan"
   // model specific priors
-  if (mod == 2) { // BYM
+  if (model_type == 2) {  // BYM
     target+= normal_lpdf(theta_raw[1] | 0, 1);  // unstructured (random) effect
     if (prior_dist_rho == 1)
-      target+= beta_lpdf(rho | shape1_rho, shape2_rho);
-    /* else prior_dist_rho is 0 and nothing is added */
-    if (prior_dist_tau == 1)
-      target+= normal_lpdf(tau | prior_mean_tau, prior_scale_tau);
-    else if (prior_dist_tau == 2)
-      target+= student_t_lpdf(tau | prior_df_tau, prior_mean_tau, prior_scale_tau);
+      target+= beta_lpdf(rho[1] | shape1_rho, shape2_rho);
     /* else prior_dist_tau is 0 and nothing is added */
   }
-  else { // besag
-    if (prior_dist_rho == 1)
-      target+= normal_lpdf(rho | prior_mean_rho, prior_scale_rho);
-    else if (prior_dist_rho == 2)
-      target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
-    /* else prior_dist_rho is 0 and nothing is added */
+  // prior on overall spatial variation
+  if (prior_dist_tau == 1)
+    target+= normal_lpdf(tau | prior_mean_tau, prior_scale_tau);
+  else if (prior_dist_tau == 2)
+    target+= student_t_lpdf(tau | prior_df_tau, prior_mean_tau, prior_scale_tau);
+  else if (prior_dist_tau == 3)
+    target+= exponential_lpdf(tau | prior_scale_tau);
+  /* else prior_dist_tau is 0 and nothing is added */
+  // priors on auxilliary parameters (Log-priors)
+  if (has_aux == 1) {
+    #include "priors_aux.stan"
   }
-  // priors on aux
-  #include "priors_aux.stan"
 }
 generated quantities {
   real mean_PPD = 0;
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
new file mode 100644
index 000000000..c1de86a74
--- /dev/null
+++ b/vignettes/spatial.Rmd
@@ -0,0 +1,208 @@
+---
+title: "CAR Spatial Models with rstanarm"
+author: "Imad Ali, Jonah Gabry and Ben Goodrich"
+date: "`r Sys.Date()`"
+output: 
+  html_vignette: 
+    toc: yes
+params:
+  EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true")
+---
+<!--
+%\VignetteEngine{knitr::rmarkdown}
+%\VignetteIndexEntry{stan_betareg: Models for Rate/Proportion Data}
+-->
+```{r, child="children/SETTINGS-knitr.txt"}
+```
+```{r, child="children/SETTINGS-gg.txt"}
+```
+```{r, child="children/SETTINGS-rstan.txt"}
+```
+```{r, child="children/SETTINGS-loo.txt"}
+```
+
+# Introduction
+
+This vignette explains how to account for spatial variation with conditional autoregressive (CAR) models when modeling discrete or continuous outcomes using the `stan_besag` and `stan_bym` functions in the __rstanarm__ package. The `stan_besag` function allows you to model space as an intrinsic conditional autoregressive model (ICAR) and the `stan_bym` is a variant of the Besag, York, Mollie (YEAR) model where the priors are more interpretable.
+
+```{r, child="children/four_steps.txt"}
+```
+
+Steps 3 and 4 are covered in more depth by the vignette entitled ["How to Use
+the __rstanarm__ Package"](rstanarm.html). This vignette focuses on Step 1 when
+the likelihood is the product of beta distributions.
+
+# Likelihood
+
+The likelihoods supported in each function include Gaussian, Binomial, Poisson, Negative Binomial, and Gamma. For details on the link functions available and the contribution to the posterior of each of these likelihoods consult the appropriate vignettes. Depending on the choice of likelihood there may or may not be an additional auxilliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data, whereas in a Binomial likelihood this would be omitted from the posterior). So the general form of the likelihood is,
+$$
+\mathcal{L}(\gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \gamma)
+$$
+
+Since it is straightforward to apply the modeler's choice of likelihood and link function we focus on the linear predictor. The linear predictor takes the following form,
+$$
+\boldsymbol{\eta} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi}
+$$
+where $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect. Here, $N$ denotes the number of spatial regions. The construction of $\boldsymbol{\psi}$ depends on the model, and this is discussed in the relevant sections below.
+
+# GMRF Hierarchical Component
+
+The core of the CAR models require that you define the spatial component as a Gaussian Markov Random Field (GMRF). The random vector $\boldsymbol{\phi}$ is a GMRF with respect to the graph $\mathcal{G} = (\mathcal{V} = \{1,\ldots,n\},\mathcal{E})$ with mean vector $\boldsymbol{\mu}$ and precision matrix $\mathbf{W}$ if its probability density function takes the precision form of the multivariate normal distribution,
+$$
+f(\boldsymbol{\phi} | \boldsymbol{\mu}) = (2\pi)^{-n/2} |\mathbf{W}|^{1/2}
+\exp\bigg( -\frac{1}{2}(\boldsymbol{\phi-\mu})^{\top}\mathbf{W}(\boldsymbol{\phi-\mu}) \bigg)
+$$
+and $W_{i,j} \neq 0$ when $\{i,j\}\in\mathcal{E}$ for all $i \neq j$. Here, $\mathcal{V}$ refers to the verticies on the graph (i.e. the spatial units) and $\mathcal{E}$ refers to the edges on the graph (i.e. the spatial units that are neighbors). So $\mathbf{W}$ is an $N$-by-$N$ matrix  (with zeros on the diagonal) which describes spatial adjacency. 
+
+Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\mathbf{Q} = \mbox{diag}(\mathbf{W1}) - W$ (which is guaranteed to be positive semi-definite) is used as the precision matrix.
+
+One interpretation of CAR models is that they are in fact hierarchical models as the spatial component enters hierarchically. This is evident in the description of the model priors below.
+
+# Besag (ICAR) Spatial Priors
+
+The `stan_besag` function fits the data to an ICAR model. This means that the spatial effect enters the linear predictor as
+$$
+\begin{align}
+\boldsymbol{\psi} = \boldsymbol{\phi} \\
+f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\rho\mathbf{Q}|^{1/2}
+\exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\rho\mathbf{Q}\boldsymbol{\phi} \bigg)
+\end{align}
+$$
+
+where $\rho$ is a scalar that controls the overall spatial variation and has an appropriate hyperprior distribution. 
+
+# BYM Spatial Priors
+
+The `stan_bym` model fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and unstructured (random) effect,
+$$
+\begin{align}
+\boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
+f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
+\exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\mathbf{Q}\boldsymbol{\phi} \bigg) \\
+f(\theta_i) &= (2\pi\sigma^2)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
+\end{align}
+$$
+Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard normal
+
+# Posterior
+
+Combining these components yields the following posteriors. For `stan_besag` we have,
+$$
+f(\boldsymbol{\beta},\boldsymbol{\phi}, \rho, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
+\prod_{i=1}^N f(y_i | \gamma) \times 
+\prod_{k=1}^K f(\beta_k) \times
+f(\boldsymbol{\phi} | \mathbf{Q}, \rho) \times
+f(\rho) \times
+f(\lambda)
+$$
+
+and for `stan_bym` we have,
+$$
+f(\boldsymbol{\beta},\boldsymbol{\phi}, \boldsymbol{\theta}, \rho, \tau, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
+\prod_{i=1}^N f(y_i | \gamma) \times 
+\prod_{k=1}^K f(\beta_k) \times
+f(\boldsymbol{\phi} | \mathbf{Q}, \rho) \times
+\prod_{k=1}^K f(\theta_i) \times
+f(\rho) \times
+f(\tau) \times
+f(\lambda)
+$$
+
+# An Example Using Simulated Data on a Lattice
+
+As an example we use spatial units defined on a lattice. Below we plot a GMRF of 100 spatial units available in the rstanarm package.
+
+```{r load-lattice, fig.align='center', fig.height=5}
+library(spdep)
+# Load the preconstruced lattice/GMRF
+data("lattice30", package = "rstanarm")
+# plot the GMRF
+var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
+spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
+       col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
+```
+
+Now we simulate predictors and a binomial outcome for the ICAR model.
+
+```{r simulate-data}
+# Convert a spatial polygon to an N-by-N weight matrix
+sp2weightmatrix <- function(spatialpolygon) {
+  spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+}
+# Neighborhood matrix
+W <- sp2weightmatrix(grid_sim)
+# Simulate predictors and outcome
+x <- rnorm(nrow(W), 2, 1)
+z <- rnorm(nrow(W), -1, 1)
+trials <- rep(10, nrow(W))
+spatial_data <- data.frame(x, z, phi = grid_sim@data$gmrf, trials)
+eta <- binomial(link="logit")$linkinv(0.5 + 0.4 * x + 0.8 * z + spatial_data$phi)
+spatial_data$y <- rbinom(nrow(W), trials, eta)
+table(spatial_data$y)
+```
+
+Plotting the outcome data on the lattice gives us a better understanding of its spatial variation.
+
+```{r plot-outcome}
+grid_sim@data$y <- spatial_data$y
+var_range_y <- seq(min(grid_sim@data$y), max(grid_sim@data$y) + 1, by = 1)
+spplot(grid_sim, "y", at = var_range_y, main = expression(y),
+       col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y) + 1))
+```
+
+Now we can fit the model with `stan_besag`.
+
+```{r fit-besag, results = "hide"}
+library(rstanarm)
+fit_besag <- stan_besag(y ~ 1 + x + z, data = spatial_data,
+                        prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
+                        family = binomial(link="logit"), trials = spatial_data$trials,
+                        chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
+fit_besag_bad <- stan_besag(y ~ 1 + x + I(x^2), data = spatial_data,
+                            prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
+                            family = binomial(link="logit"), trials = spatial_data$trials,
+                            chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
+print(fit_besag)
+print(fit_besag_bad)
+```
+
+As a two-dimensional posterior predictive check we can plot the posterior predictions on the lattice using the `posterior_predict` function.
+```{r ppcheck-1d, fig.align='center', fig.height=5}
+grid_sim@data$y_pred <- colMeans(posterior_predict(fit_besag))
+var_range_y_pred <- seq(min(grid_sim@data$y_pred), max(grid_sim@data$y_pred) + 1, by = 1)
+spplot(grid_sim, "y", at = var_range_y_pred, main = expression(y[pred]),
+       col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y_pred) + 1))
+```
+
+Alternatively we can look at the conventional one-dimensional posterior predictive check with the `pp_check` function.
+```{r ppcheck-1d, fig.align='center', fig.height=5}
+pp_check(fit_besag)
+
+```
+
+Compare model predictive performance using the [loo](http://mc-stan.org/loo) package. 
+
+```{r loo-besag}
+library(loo)
+loo_besag <- loo(fit_besag)
+loo_besag_bad <- loo(fit_besag_bad)
+print(loo_besag)
+print(loo_besag_bad)
+```
+
+```{r loo-besag-compare}
+compare(loo_besag, loo_besag_bad)
+```
+
+One way to 
+
+# References
+
+Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. _arXiv_ preprint	arXiv:1601.01180.
+
+Rue, H. and Held, L. (2005) _Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)._ Chapman & Hall/CRC, Boca Raton, FL.
+
+Rue, H. and Martino, S. (2007) Approximate Bayesian inference for hierarchical Gaussian Markov random fields models. _Journal of Statistical Planning and Inference._ Vol. 137, p3177-3192.
+
+Simpson, D., Rue, H., Martins, T.G., Riebler, A. and Sorbye, S.H. (2015). Penalising model component complexity: A principled, practical approach to constructing priors. _arXiv_ preprint	arXiv:1403.4630.
+

From 81cf7f6f9ad7d706985bca77167a702e6a0e55f7 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 12 Aug 2017 20:49:52 -0400
Subject: [PATCH 23/59] updating documentation/vignette for CAR models

---
 R/stan_besag.R        | 40 +++++++++++++++++++----------
 R/stan_bym.R          | 47 ++++++++++++++++++++-------------
 R/stan_spatial.fit.R  | 11 ++++----
 vignettes/spatial.Rmd | 60 ++++++++++++++++++++++++-------------------
 4 files changed, 94 insertions(+), 64 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 5f15a3d08..b019b6464 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -15,19 +15,22 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Bayesian intrinsic conditional autoregressive (ICAR) models via Stan
+#' Bayesian spatial ICAR models via Stan
 #'
 #' Spatial regression modeling with an intrinsic conditional autoregressive (ICAR) prior.
 #' 
 #' @rdname stan_besag
 #' @export
 #' 
+#' @templateVar fun stan_besag
+#' @templateVar fitfun stan_spatial.fit
+#' @templateVar pkg rstanarm
+#' @templateVar pkgfun stan_glm
+#' @templateVar sameargs family 
 #' @template return-stanreg-object
 #' @template return-stanfit-object
-#' @template see-also
 #' @template args-formula-data-subset
 #' @template args-same-as
-#' @template args-same-as-rarely
 #' @template args-x-y
 #' @template args-dots
 #' @template args-prior_intercept
@@ -37,26 +40,32 @@
 #' @template args-adapt_delta
 #' @template args-QR
 #' 
-#' @param family Distribution associated with the outcome. Gaussian, Binomial,
-#' and Poisson families are supported.
-#' @param trials If \code{family = binomial()} then a vector of trials (equal
-#' in length to the outcome) must be declared.
+#' @param trials If \code{family = binomial()} then a vector of trials (equal in
+#'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_tau The prior distribution on the variance of the non-centered structured (spatial) effect.
-#' 
-#' @details The \code{stan_besag} model is similar to the analogous model in
-#'   R-INLA. However, instead of using the integrated Laplace approximation
+#' @param prior_tau The prior distribution on the variance of the non-centered
+#'   structured (spatial) effect.
+#' @param order Order of the spatial random walk. Specifying \code{order = 2}
+#'   will smooth the spatial variation. The default is \code{order = 1}.
+#'   
+#' @details The \code{stan_besag} model is similar to the Besag model in R-INLA.
+#'   However, instead of using the integrated nested Laplace approximation
 #'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm}
 #'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
-#'   regression coefficients, and the relevant scale parameters. The
-#'   \code{stan_besag} function calls the workhorse \code{stan_spatial.fit}
-#'   function, but it is also possible to call the latter directly.
+#'   regression coefficients, overall spatial variation, and any applicable
+#'   auxiliary parameters. The \code{stan_besag} function calls the workhorse
+#'   \code{stan_spatial.fit} function, but it is also possible to call the
+#'   latter directly.
 #'   
 #' @seealso The vignette for \code{stan_besag}.
 #' 
 #' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An
 #'   intuitive Bayesian spatial model for disease mapping that accounts for
 #'   scaling. arXiv preprint	arXiv:1601.01180.
+#'   
+#'   Besag, J. (1974). Spatial Interaction and the Statistical Analysis of
+#'   Lattice Systems. Journal of the Royal Statistical Society. Vol 36, No 2,
+#'   p192-236.
 #' 
 #' @examples 
 #' ### Simulated Data on a Lattice
@@ -92,6 +101,7 @@ stan_besag <- function(formula,
                      data,
                      trials = NULL,
                      W,
+                     order = c(1,2),
                      ...,
                      prior = normal(), prior_intercept = normal(),
                      prior_tau = normal(), prior_aux = NULL,
@@ -104,6 +114,7 @@ stan_besag <- function(formula,
     stop(paste("Please install and load the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
+  order <- match.arg(order)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
   mt <- terms(formula, data = data)
@@ -114,6 +125,7 @@ stan_besag <- function(formula,
                               trials = trials,
                               family = family,
                               stan_function = stan_function,
+                              order = order,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 03372be62..99b5c0e60 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -15,18 +15,21 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Bayesian conditional autoregressive (CAR) BYM models via Stan
+#' Bayesian spatial CAR BYM models via Stan
 #'
-#' Spatial regression modeling with the Besag, York, Mollie conditional autoregressive (CAR) prior.
+#' Spatial regression modeling with a variant of the Besag, York, Mollie (BYM) conditional autoregressive (CAR) prior.
 #' 
 #' @export
 #' 
+#' @templateVar fun stan_bym
+#' @templateVar fitfun stan_spatial.fit
+#' @templateVar pkg rstanarm
+#' @templateVar pkgfun stan_glm
+#' @templateVar sameargs family 
 #' @template return-stanreg-object
 #' @template return-stanfit-object
-#' @template see-also
 #' @template args-formula-data-subset
 #' @template args-same-as
-#' @template args-same-as-rarely
 #' @template args-x-y
 #' @template args-dots
 #' @template args-prior_intercept
@@ -36,27 +39,28 @@
 #' @template args-adapt_delta
 #' @template args-QR
 #' 
-#' @param family Distribution associated with the outcome. Gaussian, Binomial,
-#'   and Poisson families are supported.
 #' @param trials If \code{family = binomial()} then a vector of trials (equal in
 #'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_rho The prior on the proportion of the marginal variance that is
-#'   explained by the structured (spatial) effect. The hyperparameter \code{rho}
-#'   is in the unit interval so users have the option of declaring a Beta prior 
-#'   distribution or a flat prior. A prior distribution with more mass around 1 
-#'   is analogous to the prior belief that there exists a strong spatial 
-#'   relationship on the graph.
 #' @param prior_tau The prior on the marginal variance contribution of the
 #'   structured (spatial) and unstructured (random) effect.
+#' @param prior_rho The prior on the proportion of the marginal variance that is
+#'   explained by the structured (spatial) effect. The hyperparameter \code{rho}
+#'   is on the unit interval so users have the option of declaring a Beta prior 
+#'   distribution or a flat prior. A prior distribution with most of the mass
+#'   around 1 is analogous to the prior belief that there exists a strong
+#'   spatial relationship on the graph.
+#' @param order Order of the spatial random walk. Specifying \code{order = 2}
+#'   will smooth the spatial variation. The default is \code{order = 1}.
 #'   
-#' @details The \code{stan_bym} model is similar to the analogous model in
-#'   R-INLA. However, instead of using the integrated Laplace approximation
-#'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm}
+#' @details The \code{stan_bym} model is similar to the BYM2 model in R-INLA.
+#'   However, instead of using the integrated nested Laplace approximation 
+#'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm} 
 #'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
-#'   regression coefficients, and the relevant scale/mixing parameters. The
-#'   \code{stan_bym} function calls the workhorse \code{stan_spatial.fit}
-#'   function, but it is also possible to call the latter directly.
+#'   regression coefficients, spatial mixing parameter, overall spatial
+#'   variation, and any applicable auxiliary parameters. The \code{stan_bym}
+#'   function calls the workhorse \code{stan_spatial.fit} function, but it is
+#'   also possible to call the latter directly.
 #' 
 #' @seealso The vignette for \code{stan_bym}.
 #' 
@@ -64,6 +68,10 @@
 #'   intuitive Bayesian spatial model for disease mapping that accounts for
 #'   scaling. arXiv preprint	arXiv:1601.01180.
 #'   
+#'   Besag, J., York, J. and Mollié, A. (1991). Bayesian image restoration, with
+#'   two applications in spatial statistics. Annals of the Institute of
+#'   Statistical Mathematics. Vol. 43, No. 01, p1-20.
+#'   
 #'   Simpson, D., Rue, H., Martins, T.G., Riebler, A. and Sorbye, S.H. (2015).
 #'   Penalising model component complexity: A principled, practical approach to
 #'   constructing priors. arXiv preprint	arXiv:1403.4630.
@@ -101,6 +109,7 @@ stan_bym <- function(formula,
                         data,
                         trials = NULL,
                         W,
+                        order = c(1,2),
                         ...,
                         prior = normal(), prior_intercept = normal(),
                         prior_tau = normal(), prior_rho = beta(0.5,0.5), prior_aux = NULL,
@@ -113,6 +122,7 @@ stan_bym <- function(formula,
     stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
+  order <- match.arg(order)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
   mt <- terms(formula, data = data)
@@ -128,6 +138,7 @@ stan_bym <- function(formula,
                               trials = trials,
                               family = family,
                               stan_function = stan_function,
+                              order = order,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 90886f9ed..a708e2baa 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -15,11 +15,11 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Workhorse function for CAR spatial models.
+#' Workhorse function for CAR models.
 #' 
-#' Both \code{stan_besag} and \code{stan_bym} call \code{stan_spatial.fit} to fit
-#' the appropriate spatial model. See the documentation for either model for further
-#' details on the arguments of \code{stan_spatial.fit}.
+#' Both \code{stan_besag} and \code{stan_bym} call \code{stan_spatial.fit} to
+#' fit the appropriate spatial model. See the documentation for either modeling
+#' function for further details on the arguments of \code{stan_spatial.fit}.
 #' 
 #' @export
 #' 
@@ -28,6 +28,7 @@ stan_spatial.fit <- function(x, y, w,
                              stan_function = c("stan_besag", "stan_bym"),
                              family = NULL,
                              trials = NULL,
+                             order = c(1,2),
                              ...,
                              prior = normal(), prior_intercept = normal(),
                              prior_tau = normal(), prior_aux = NULL, prior_rho = NULL,
@@ -35,7 +36,7 @@ stan_spatial.fit <- function(x, y, w,
                              algorithm = c("sampling", "meanfield", "fullrank"),
                              adapt_delta = NULL,
                              QR = FALSE) {
-  # check that W is appropriate
+  # convert W to a sparse matrix if not already sparse.
   
   algorithm <- match.arg(algorithm)
 
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index c1de86a74..5d758597d 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -21,7 +21,7 @@ params:
 ```{r, child="children/SETTINGS-loo.txt"}
 ```
 
-# Introduction
+## Introduction
 
 This vignette explains how to account for spatial variation with conditional autoregressive (CAR) models when modeling discrete or continuous outcomes using the `stan_besag` and `stan_bym` functions in the __rstanarm__ package. The `stan_besag` function allows you to model space as an intrinsic conditional autoregressive model (ICAR) and the `stan_bym` is a variant of the Besag, York, Mollie (YEAR) model where the priors are more interpretable.
 
@@ -32,22 +32,24 @@ Steps 3 and 4 are covered in more depth by the vignette entitled ["How to Use
 the __rstanarm__ Package"](rstanarm.html). This vignette focuses on Step 1 when
 the likelihood is the product of beta distributions.
 
-# Likelihood
+## Likelihood
 
-The likelihoods supported in each function include Gaussian, Binomial, Poisson, Negative Binomial, and Gamma. For details on the link functions available and the contribution to the posterior of each of these likelihoods consult the appropriate vignettes. Depending on the choice of likelihood there may or may not be an additional auxilliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data, whereas in a Binomial likelihood this would be omitted from the posterior). So the general form of the likelihood is,
+The likelihoods supported in each function include Gaussian, Binomial, Poisson, Negative Binomial, and Gamma. For details on the link functions available and the contribution to the posterior of each of these likelihoods consult the appropriate [vignette](http://mc-stan.org/rstanarm/articles/index.html). 
+
+The linear predictor takes the following form,
 $$
-\mathcal{L}(\gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \gamma)
+\boldsymbol{\eta} = \alpha + \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi}
 $$
+where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect (and $N$ denotes the number of spatial units). The construction of $\boldsymbol{\psi}$ depends on the model, and this is discussed in the relevant sections below.
 
-Since it is straightforward to apply the modeler's choice of likelihood and link function we focus on the linear predictor. The linear predictor takes the following form,
+Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). Thus, for some probability density/mass function $f$ the general form of the likelihood is,
 $$
-\boldsymbol{\eta} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi}
+\mathcal{L}(\alpha, \boldsymbol{\beta}, \gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \alpha, \boldsymbol{\beta}, \gamma )
 $$
-where $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect. Here, $N$ denotes the number of spatial regions. The construction of $\boldsymbol{\psi}$ depends on the model, and this is discussed in the relevant sections below.
 
-# GMRF Hierarchical Component
+## GMRF Hierarchical Component
 
-The core of the CAR models require that you define the spatial component as a Gaussian Markov Random Field (GMRF). The random vector $\boldsymbol{\phi}$ is a GMRF with respect to the graph $\mathcal{G} = (\mathcal{V} = \{1,\ldots,n\},\mathcal{E})$ with mean vector $\boldsymbol{\mu}$ and precision matrix $\mathbf{W}$ if its probability density function takes the precision form of the multivariate normal distribution,
+CAR models require that you define the spatial component as a Gaussian Markov Random Field (GMRF). The random vector $\boldsymbol{\phi}$ is a GMRF with respect to the graph $\mathcal{G} = (\mathcal{V} = \{1,\ldots,n\},\mathcal{E})$ with mean vector $\boldsymbol{\mu}$ and precision matrix $\mathbf{W}$ if its probability density function takes the precision form of the multivariate normal distribution,
 $$
 f(\boldsymbol{\phi} | \boldsymbol{\mu}) = (2\pi)^{-n/2} |\mathbf{W}|^{1/2}
 \exp\bigg( -\frac{1}{2}(\boldsymbol{\phi-\mu})^{\top}\mathbf{W}(\boldsymbol{\phi-\mu}) \bigg)
@@ -56,24 +58,22 @@ and $W_{i,j} \neq 0$ when $\{i,j\}\in\mathcal{E}$ for all $i \neq j$. Here, $\ma
 
 Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\mathbf{Q} = \mbox{diag}(\mathbf{W1}) - W$ (which is guaranteed to be positive semi-definite) is used as the precision matrix.
 
-One interpretation of CAR models is that they are in fact hierarchical models as the spatial component enters hierarchically. This is evident in the description of the model priors below.
-
-# Besag (ICAR) Spatial Priors
+## Besag (ICAR) Spatial Prior
 
 The `stan_besag` function fits the data to an ICAR model. This means that the spatial effect enters the linear predictor as
 $$
 \begin{align}
 \boldsymbol{\psi} = \boldsymbol{\phi} \\
-f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\rho\mathbf{Q}|^{1/2}
-\exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\rho\mathbf{Q}\boldsymbol{\phi} \bigg)
+f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\tau\mathbf{Q}|^{1/2}
+\exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\tau\mathbf{Q}\boldsymbol{\phi} \bigg)
 \end{align}
 $$
 
 where $\rho$ is a scalar that controls the overall spatial variation and has an appropriate hyperprior distribution. 
 
-# BYM Spatial Priors
+## Variant of the BYM Spatial Prior
 
-The `stan_bym` model fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and unstructured (random) effect,
+The `stan_bym` model fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and thex unstructured (random) effect,
 $$
 \begin{align}
 \boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
@@ -82,13 +82,13 @@ f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
 f(\theta_i) &= (2\pi\sigma^2)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
 \end{align}
 $$
-Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard normal
+Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard normal.
 
-# Posterior
+## Posterior
 
 Combining these components yields the following posteriors. For `stan_besag` we have,
 $$
-f(\boldsymbol{\beta},\boldsymbol{\phi}, \rho, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
+f(\alpha, \boldsymbol{\beta},\boldsymbol{\phi}, \rho, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
 \prod_{i=1}^N f(y_i | \gamma) \times 
 \prod_{k=1}^K f(\beta_k) \times
 f(\boldsymbol{\phi} | \mathbf{Q}, \rho) \times
@@ -98,7 +98,7 @@ $$
 
 and for `stan_bym` we have,
 $$
-f(\boldsymbol{\beta},\boldsymbol{\phi}, \boldsymbol{\theta}, \rho, \tau, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
+f(\alpha, \boldsymbol{\beta},\boldsymbol{\phi}, \boldsymbol{\theta}, \rho, \tau, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
 \prod_{i=1}^N f(y_i | \gamma) \times 
 \prod_{k=1}^K f(\beta_k) \times
 f(\boldsymbol{\phi} | \mathbf{Q}, \rho) \times
@@ -108,14 +108,15 @@ f(\tau) \times
 f(\lambda)
 $$
 
-# An Example Using Simulated Data on a Lattice
+## An Example Using Simulated Data on a Lattice
 
 As an example we use spatial units defined on a lattice. Below we plot a GMRF of 100 spatial units available in the rstanarm package.
 
 ```{r load-lattice, fig.align='center', fig.height=5}
 library(spdep)
 # Load the preconstruced lattice/GMRF
-data("lattice30", package = "rstanarm")
+data("lattice", package = "rstanarm")
+grid_sim <- grid_sim30
 # plot the GMRF
 var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
 spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
@@ -167,7 +168,7 @@ print(fit_besag_bad)
 ```
 
 As a two-dimensional posterior predictive check we can plot the posterior predictions on the lattice using the `posterior_predict` function.
-```{r ppcheck-1d, fig.align='center', fig.height=5}
+```{r ppcheck-2d, fig.align='center', fig.height=5}
 grid_sim@data$y_pred <- colMeans(posterior_predict(fit_besag))
 var_range_y_pred <- seq(min(grid_sim@data$y_pred), max(grid_sim@data$y_pred) + 1, by = 1)
 spplot(grid_sim, "y", at = var_range_y_pred, main = expression(y[pred]),
@@ -177,7 +178,6 @@ spplot(grid_sim, "y", at = var_range_y_pred, main = expression(y[pred]),
 Alternatively we can look at the conventional one-dimensional posterior predictive check with the `pp_check` function.
 ```{r ppcheck-1d, fig.align='center', fig.height=5}
 pp_check(fit_besag)
-
 ```
 
 Compare model predictive performance using the [loo](http://mc-stan.org/loo) package. 
@@ -194,11 +194,17 @@ print(loo_besag_bad)
 compare(loo_besag, loo_besag_bad)
 ```
 
-One way to 
+## Smoothing the Spatial Random Walk
+
+In some cases modeling the GMRF spatial component with the precsion matrix $\mathbf{Q}$ leads to rough spatial varation. This occurs when dealing with spatial units on a lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation. Below we visually illustrate this result with a random walk in one-dimension using the `stan_rw` function with the argument `smooth = TRUE`.
+
+## References
+
+Besag, J. (1974). Spatial Interaction and the Statistical Analysis of Lattice Systems. _Journal of the Royal Statistical Society._ Vol. 36, No. 2, p192-236.
 
-# References
+Besag, J., York, J. and Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. _Annals of the Institute of Statistical Mathematics._ Vol. 43, No. 01, p1-20.
 
-Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. _arXiv_ preprint	arXiv:1601.01180.
+Riebler, A., Sorbye, S.H., Simpson, D. and Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. _arXiv_ preprint	arXiv:1601.01180.
 
 Rue, H. and Held, L. (2005) _Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)._ Chapman & Hall/CRC, Boca Raton, FL.
 

From b65253c4c9ba7683e713ddd616af8caafe872056 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 12 Aug 2017 21:37:26 -0400
Subject: [PATCH 24/59] adding 2nd order spatial random walks

---
 R/stan_besag.R       |  3 +--
 R/stan_bym.R         |  3 +--
 R/stan_spatial.fit.R | 64 +++++++++++++++++++++++++++++---------------
 exec/spatial.stan    | 31 ++++++++++++++-------
 4 files changed, 65 insertions(+), 36 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index b019b6464..c57cacd47 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -101,7 +101,7 @@ stan_besag <- function(formula,
                      data,
                      trials = NULL,
                      W,
-                     order = c(1,2),
+                     order = 1,
                      ...,
                      prior = normal(), prior_intercept = normal(),
                      prior_tau = normal(), prior_aux = NULL,
@@ -114,7 +114,6 @@ stan_besag <- function(formula,
     stop(paste("Please install and load the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
-  order <- match.arg(order)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
   mt <- terms(formula, data = data)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 99b5c0e60..fd08a16d8 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -109,7 +109,7 @@ stan_bym <- function(formula,
                         data,
                         trials = NULL,
                         W,
-                        order = c(1,2),
+                        order = 1,
                         ...,
                         prior = normal(), prior_intercept = normal(),
                         prior_tau = normal(), prior_rho = beta(0.5,0.5), prior_aux = NULL,
@@ -122,7 +122,6 @@ stan_bym <- function(formula,
     stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
-  order <- match.arg(order)
   family <- validate_family(family)
   mf <- model.frame(mc, data)
   mt <- terms(formula, data = data)
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index a708e2baa..697f78ea8 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -87,6 +87,9 @@ stan_spatial.fit <- function(x, y, w,
   else if (stan_function == "stan_bym")
     model_type <- 2
   
+  if (!(order %in% c(1,2)))
+    stop("Argument 'order' must be 1 or 2.")
+  
   x_stuff <- center_x(x, sparse = FALSE)
   for (i in names(x_stuff)) # xtemp, xbar, has_intercept
     assign(i, x_stuff[[i]])
@@ -228,33 +231,29 @@ stan_spatial.fit <- function(x, y, w,
                     global_prior_scale_for_intercept,
                     num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0),
                     prior_dist_rho = prior_dist_for_rho)
-
-  # create scaling_factor a la Dan Simpson
-  create_scaling_factor <- function(dat) {
-    edges <- dat$edges
-    #Build the adjacency matrix
-    adj.matrix <- Matrix::sparseMatrix(i=edges[,1],j=edges[,2],x=1,symmetric=TRUE)
-    #The ICAR precision matrix (note! This is singular)
-    Q <-  Matrix::Diagonal(dat$N, Matrix::rowSums(adj.matrix)) - adj.matrix
-    
-    #Add a small jitter to the diagonal for numerical stability (optional but recommended)
-    Q_pert <- Q + Matrix::Diagonal(dat$N) * max(Matrix::diag(Q)) * sqrt(.Machine$double.eps)
-    
-    # Compute the diagonal elements of the covariance matrix subject to the 
-    # constraint that the entries of the ICAR sum to zero.
-    # See the function help for further details.
-    Q_inv <- inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
-    
-    # Compute the geometric mean of the variances, which are on the diagonal of Q.inv
-    scaling_factor <- exp(mean(log(Matrix::diag(Q_inv))))
-    return(scaling_factor)
-  }
   
   if (stan_function == "stan_bym")
     standata$scaling_factor <- create_scaling_factor(standata)
   else
     standata$scaling_factor <- 0
-
+  
+  standata$order <- order
+  if (order == 2) {
+    Q <- diag(rowSums(W) - W)
+    Q <- Q %*% Q
+    sparse_stuff <- rstan::extract_sparse_parts(Q)
+    standata$Q_n <- as.array(length(sparse_stuff$w), dim = 1)
+    standata$w <- sparse_stuff$w
+    standata$v <- sparse_stuff$v
+    standata$u <- sparse_stuff$u
+  }
+  if (order == 1) {
+    standata$Q_n <- array(0, dim = c(0))
+    standata$w <- array(0, dim = c(0))
+    standata$v <- array(0, dim = c(0))
+    standata$u <- array(0, dim = c(0))
+  }
+  
   pars <- c(if (has_intercept) "alpha", "beta", if(model_type == 2) "rho", "tau", if(has_aux) "aux",
             "mean_PPD", "psi")
 
@@ -339,6 +338,27 @@ stan_spatial.fit <- function(x, y, w,
   }
 }
 
+# create scaling_factor a la Dan Simpson
+create_scaling_factor <- function(dat) {
+  edges <- dat$edges
+  #Build the adjacency matrix
+  adj.matrix <- Matrix::sparseMatrix(i=edges[,1],j=edges[,2],x=1,symmetric=TRUE)
+  #The ICAR precision matrix (note! This is singular)
+  Q <-  Matrix::Diagonal(dat$N, Matrix::rowSums(adj.matrix)) - adj.matrix
+  
+  #Add a small jitter to the diagonal for numerical stability (optional but recommended)
+  Q_pert <- Q + Matrix::Diagonal(dat$N) * max(Matrix::diag(Q)) * sqrt(.Machine$double.eps)
+  
+  # Compute the diagonal elements of the covariance matrix subject to the 
+  # constraint that the entries of the ICAR sum to zero.
+  # See the function help for further details.
+  Q_inv <- inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
+  
+  # Compute the geometric mean of the variances, which are on the diagonal of Q.inv
+  scaling_factor <- exp(mean(log(Matrix::diag(Q_inv))))
+  return(scaling_factor)
+}
+
 # Summarize spatial prior
 
 summarize_spatial_prior <- function(user_prior,
diff --git a/exec/spatial.stan b/exec/spatial.stan
index f09c8cc11..3bbc65609 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -12,20 +12,28 @@ data {
   int<lower=0> K;                 // number of predictors (inc intercept)
   matrix[N,K] X;                  // model matrix
   int<lower=1,upper=5> family;
+  int link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
-  int<lower=1,upper=2> model_type;       // Besag = 1; BYM = 2
+  int<lower=1,upper=2> model_type;       // 0 = 1D RW; Besag = 1; BYM = 2
   int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
-  int<lower=0> trials[family == 4 ? N : 0];         // binomial trials (0 1d array if not applicable)
-  int y_int[is_continuous == 1 ? 0 : N];                   // outcome
-  vector[is_continuous == 1 ? N : 0] y_real;                 // outcome
-  int link;
+  int<lower=0> trials[family == 4 ? N : 0];
+  int y_int[is_continuous == 1 ? 0 : N];
+  vector[is_continuous == 1 ? N : 0] y_real;
+  // pairwise difference version of CAR
   int E_n;                        // number of adjacency pairs
   int edges[E_n, 2];              // adjacency pairs
+  // matrix version of CAR
+  int<lower=1,upper=2> order;
+  int<lower=0> Q_n[order == 2];
+  vector[order == 2 ? Q_n[1] : 0] w;
+  int v[order == 2 ? Q_n[1] : 0];
+  int u[order == 2 ? N+1 : 0];
+  // prior stuff
   int<lower=0,upper=1> prior_dist_rho;
-  real<lower=0> shape1_rho;        // priors
-  real<lower=0> shape2_rho;        // priors
+  real<lower=0> shape1_rho;
+  real<lower=0> shape2_rho;
   real scaling_factor;
   int<lower=0> prior_dist_for_intercept;
   int<lower=0> prior_dist;
@@ -39,8 +47,8 @@ data {
   real prior_mean_tau;
   real<lower=0> prior_scale_tau;
   real<lower=0> prior_df_tau;
-  real<lower=0> global_prior_df;    // for hs priors only
-  real<lower=0> global_prior_scale; // for hs priors only
+  real<lower=0> global_prior_df;
+  real<lower=0> global_prior_scale;
   int<lower=2> num_normals[prior_dist == 7 ? K : 0];
   int<lower=0,upper=3> prior_dist_for_aux;
   real<lower=0> prior_mean_for_aux;
@@ -114,7 +122,10 @@ model {
     target += GammaReg(y_real, eta, aux, link, sum_log_y);
   }
   // prior on spatial parameter vector (GMRF)
-  target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
+  if (order == 1)
+    target += -0.5 * dot_self(phi[edges[,1]] - phi[edges[,2]]);
+  else if (order == 2)
+    target+= -0.5 * dot_product(phi, csr_matrix_times_vector(N, N, w, v, u, phi));
   // priors on coefficients
   #include "priors.stan"
   // model specific priors

From a039e2ab4d1dff5c64ab852ae22e8fed81893ccc Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 12 Aug 2017 21:54:46 -0400
Subject: [PATCH 25/59] Q should be squared if 'order == 2'

---
 R/stan_spatial.fit.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 697f78ea8..1afa0abdf 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -239,7 +239,7 @@ stan_spatial.fit <- function(x, y, w,
   
   standata$order <- order
   if (order == 2) {
-    Q <- diag(rowSums(W) - W)
+    Q <- diag(rowSums(W)) - W
     Q <- Q %*% Q
     sparse_stuff <- rstan::extract_sparse_parts(Q)
     standata$Q_n <- as.array(length(sparse_stuff$w), dim = 1)

From 40b248bad7e3c5f18b765d5531c2bc8c8b853d68 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 13 Aug 2017 01:56:08 -0400
Subject: [PATCH 26/59] update stan_besag tests with inla comparisons

---
 R/datasets.R                     |  10 +-
 R/stan_besag.R                   |   2 +-
 R/stan_bym.R                     |   2 +-
 R/stan_spatial.fit.R             |  13 +-
 data/lattice.rda                 | Bin 33425 -> 44073 bytes
 exec/spatial.stan                |  33 +++-
 tests/testthat/test_stan_besag.R | 304 ++++++++++++++++++-------------
 7 files changed, 225 insertions(+), 139 deletions(-)

diff --git a/R/datasets.R b/R/datasets.R
index f5309986c..8ed79646e 100644
--- a/R/datasets.R
+++ b/R/datasets.R
@@ -139,10 +139,12 @@
 #' \item \code{educ} Years of education (head of household)
 #' }
 #' }
-#' \item{\code{lattice}}{ The \code{grid_sim10} and \code{grid_sim30}
-#' SpatialPolygonsDataFrame objects are a simulated lattice of 100 and 900
-#' spatial units, respectively. These objects contain spatially dependent
-#' observations simulated as a Gaussian Markov Random Field (GMRF).
+#' \item{\code{lattice}}{ The \code{grid_sim15} and \code{grid_sim30} 
+#' SpatialPolygonsDataFrame objects are a simulated lattice of 225 and 900 
+#' spatial units, respectively. These objects contain spatially dependent 
+#' observations simulated as a Gaussian Markov Random Field (GMRF). The data 
+#' slot in \code{grid_sim15} contains simulated outcomes using the GMRF and
+#' various supported likelihoods.
 #' 
 #' Source: Simulated data
 #' 
diff --git a/R/stan_besag.R b/R/stan_besag.R
index c57cacd47..8d3a8214a 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -73,7 +73,7 @@
 #' data("lattice", package = "rstanarm")
 #' 
 #' # plot GMRF
-#' grid_sim <- grid_sim10
+#' grid_sim <- grid_sim15
 #' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
 #' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
 #'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
diff --git a/R/stan_bym.R b/R/stan_bym.R
index fd08a16d8..868da82b2 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -82,7 +82,7 @@
 #' data("lattice", package = "rstanarm")
 #' 
 #' # plot GMRF
-#' grid_sim <- grid_sim10
+#' grid_sim <- grid_sim15
 #' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
 #' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
 #'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 1afa0abdf..9c8bd2463 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -62,7 +62,6 @@ stan_spatial.fit <- function(x, y, w,
     is_continuous <- TRUE
     y_real <- y
     y_int <- array(0, dim = c(0))
-    trials <- array(0, dim = c(0))
   }
   else {
     is_continuous <- FALSE
@@ -75,6 +74,9 @@ stan_spatial.fit <- function(x, y, w,
   else
     has_aux <- TRUE
   
+  if (family != "binomial")
+    trials <- array(0, dim = c(0))
+  
   if (family$family %in% c("binomial", "poisson", "neg_binomial_2")) {
     if(!is.integer(y_int))
       stop("Outcome must be an integer for count likelihoods.")
@@ -256,6 +258,13 @@ stan_spatial.fit <- function(x, y, w,
   
   pars <- c(if (has_intercept) "alpha", "beta", if(model_type == 2) "rho", "tau", if(has_aux) "aux",
             "mean_PPD", "psi")
+  
+  switch_aux <- switch(family$family,
+                       gaussian = "sigma",
+                       poisson = NA,
+                       neg_binomial_2 = "reciprocal_dispersion",
+                       binomial = NA,
+                       Gamma = "shape")
 
   prior_info <- summarize_spatial_prior(
     user_prior = prior_stuff,
@@ -331,7 +340,7 @@ stan_spatial.fit <- function(x, y, w,
     }
     new_names <- c(if (has_intercept) "(Intercept)", 
                    colnames(xtemp), if(model_type == 2) "rho", "tau",
-                   if(has_aux) "aux", "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
+                   if(has_aux) switch_aux, "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
 
     stanfit@sim$fnames_oi <- new_names
     return(structure(stanfit, prior.info = prior_info))
diff --git a/data/lattice.rda b/data/lattice.rda
index be66d26fb806eb13cec153eedbac27c4b4dcda53..fecb830cfb44df12c6b106cef295454ac091881a 100644
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diff --git a/exec/spatial.stan b/exec/spatial.stan
index 3bbc65609..4d2e105b9 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -6,6 +6,37 @@ functions {
   #include "binomial_likelihoods.stan"
   #include "count_likelihoods.stan"
   #include "common_functions.stan"
+  /*
+   * Calculate lower bound on intercept
+   *
+   * @param family Integer family code
+   * @param link Integer link code
+   * @return real lower bound
+   */
+  real make_lower(int family, int link) {
+    if (family == 1) return negative_infinity(); // Gaussian
+    if (family == 5) { // Gamma
+      if (link == 2) return negative_infinity(); // log
+      return 0; // identity or inverse
+    }
+    if (family == 2 || family == 3) { // Poisson or nb2
+      if (link == 1) return negative_infinity(); // log
+      return 0.0; // identity or sqrt
+    }
+    return negative_infinity();
+  }
+
+  /*
+   * Calculate upper bound on intercept
+   *
+   * @param family Integer family code
+   * @param link Integer link code
+   * @return real upper bound
+   */
+  real make_upper(int family, int link) {
+    if (family == 4 && link == 4) return 0.0;  // binomial; log
+    return positive_infinity();
+  }
 }
 data {
   int<lower=0> N;                 // number of regions
@@ -65,7 +96,7 @@ transformed data {
   else hs = 0;
 }
 parameters {
-  real gamma[has_intercept];  // raw intercept
+  real<lower=make_lower(family, link), upper=make_upper(family,link)> gamma[has_intercept];  // raw intercept
   vector[K] z_beta;  // standard normal term
   vector[model_type == 2? N : 0] theta_raw;        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 6e28f7301..3f380a567 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -18,139 +18,183 @@
 # tests can be run using devtools::test() or manually by loading testthat 
 # package and then running the code below possibly with options(mc.cores = 4).
 
-if (.Platform$OS.type != "windows" && require(betareg)) {
-  library(rstanarm)
-  SEED <- 12345
-  set.seed(SEED)
-  ITER <- 100
-  CHAINS <- 2
-  
-  context("stan_besag")
-  
-  source(file.path("helpers", "expect_stanreg.R"))
-  source(file.path("helpers", "SW.R"))
-  
-  data("lattice10", package = "rstanarm")
-  
-  # Convert a spatial polygon to an N-by-N weight matrix
-  sp2weightmatrix <- function(spatialpolygon) {
-    spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
-  }
-  W <- sp2weightmatrix(grid_sim)
-  x <- rnorm(nrow(W), 2, 1)
-  spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
-  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
-  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
-  spatial_data$y_pois <- rpois(nrow(W), exp(-1 + 1.4 * x + spatial_data$phi))
-  spatial_data$trials <- rep(10, nrow(W))
-  spatial_data$y_binom <- rbinom(nrow(W), spatial_data$trials, binomial(link = "logit")$linkinv(1 + 0.4 * x + spatial_data$phi))
-  
-  # test family/link combinations
-  test_that("family = 'gaussian' works", {
-    SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+library(rstanarm)
+SEED <- 12345
+set.seed(SEED)
+ITER <- 50
+CHAINS <- 2
+
+context("stan_besag")
+
+source(file.path("helpers", "expect_stanreg.R"))
+source(file.path("helpers", "SW.R"))
+
+data("lattice", package = "rstanarm")
+
+# Convert a spatial polygon to an N-by-N weight matrix
+sp2weightmatrix <- function(spatialpolygon) {
+  spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+}
+W <- sp2weightmatrix(grid_sim15)
+spatial_data <- grid_sim15@data
+
+SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
+                           W = W, iter = 100, chains = 4))
+SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                           family = binomial(link = "logit"),
+                           W = W, iter = 100, chains = 4))
+SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
+                          family = poisson(link = "log"),
+                          W = W, iter = 100, chains = 4))
+SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
+                          family = neg_binomial_2(link = "log"),
+                          W = W, iter = 100, chains = 4))
+SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                                 W = W, iter = 100, chains = 4))
+
+# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
+test_that("stan_besag estimates match INLA", {
+  inla_gauss_est <- c(-0.0475, 0.4329, 0.9830, 0.8277)
+  inla_binom_est <- c(-1.9444, 0.3340, 0.423)
+  inla_pois_est <- c(0.7801, 0.4366, 0.3448)
+  inla_nb_est <- c(0.8053, 0.4362, 0.3574, 48.6003)
+  inla_gamma_est <- c(0.8119, -1.3407, 0.559, 1.106)
+  besag_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
+  besag_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
+  besag_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
+  besag_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
+  besag_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
+  expect_equal(besag_gauss, inla_gauss_est, tol = 0.2)
+  expect_equal(besag_binom, inla_binom_est, tol = 0.2)
+  expect_equal(besag_pois, inla_pois_est, tol = 0.2)
+  expect_equal(besag_nb2, inla_nb_est, tol = 0.2)
+  expect_equal(besag_gamma, inla_gamma_est, tol = 0.2)
+})
+
+# test family/link combinations
+test_that("family = 'gaussian' works", {
+  SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
                                    W = W, iter = ITER, chains = CHAINS))
-    SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+  SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_gauss_ident)
+  expect_stanreg(fit_gauss_log)
+  expect_stanreg(fit_gauss_inv)
+})
+test_that("family = 'binomial' works", {
+  SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                   family = binomial(link = "logit"),
                                    W = W, iter = ITER, chains = CHAINS))
-    expect_stanreg(fit_gauss_ident)
-    expect_stanreg(fit_gauss_log)
-    expect_stanreg(fit_gauss_inv)
-  })
-  test_that("family = 'binomial' works", {
-    SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "logit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                      family = binomial(link = "probit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                       family = binomial(link = "cauchit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                   family = binomial(link = "log"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                       family = binomial(link = "cloglog"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    expect_stanreg(fit_binom_logit)
-    expect_stanreg(fit_binom_probit)
-    expect_stanreg(fit_binom_cauchit)
-    expect_stanreg(fit_binom_log)
-    expect_stanreg(fit_binom_cloglog)
-    
-  })
-  test_that("family = 'poisson' works", {
-    SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+  SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                    family = binomial(link = "probit"),
                                    W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-                                  W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-                                  W = W, iter = 10, chains = CHAINS))
-    expect_stanreg(fit_pois_log)
-    expect_stanreg(fit_pois_ident)
-    expect_stanreg(fit_pois_sqrt)
-  })
-  
-  # test QR 
-  test_that("QR errors when number of predictors is <= 1", {
-    expect_error(
-      stan_besag(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
-      "'QR' can only be specified when there are multiple predictors"
-    )
-  })
-  
-  test_that("QR works when number of x and/or z predictors is >= 1", {
-    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                                     W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    expect_stanreg(fit_besag)
-  })
+  SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cauchit"),
+                                   W = W, iter = ITER, chains = CHAINS))
+  SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                 family = binomial(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+  SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cloglog"),
+                                   W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_binom_logit)
+  expect_stanreg(fit_binom_probit)
+  expect_stanreg(fit_binom_cauchit)
+  expect_stanreg(fit_binom_log)
+  expect_stanreg(fit_binom_cloglog)
   
-  test_that("stan_besag works with QR = TRUE and algorithm = 'optimizing'", {
+})
+test_that("family = 'poisson' works", {
+  SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+                                W = W, iter = ITER, chains = CHAINS))
+  SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+                                W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_pois_log)
+  expect_stanreg(fit_pois_ident)
+  expect_stanreg(fit_pois_sqrt)
+})
 
-  })
-  
-  test_that("loo/waic for stan_besag works", {
-    SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "logit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
-                               family = poisson(link = "log"),
-                               W = W, iter = ITER, chains = CHAINS))
-    loo(fit_gauss)
-    loo(fit_binom)
-    loo(fit_pois)
-  })
-  
-  test_that("posterior_predict works for stan_besag", {
-    SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                               family = binomial(link = "logit"),
+# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+test_that("family = 'neg_binomial_2' works", {
+  SW(fit_nb2_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
+                                W = W, iter = ITER, chains = CHAINS))
+  SW(fit_nb2_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+                                  W = W, iter = ITER, chains = CHAINS))
+  SW(fit_nb2_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_nb2_log)
+  expect_stanreg(fit_nb2_ident)
+  expect_stanreg(fit_nb2_sqrt)
+})
+
+test_that("family = 'Gamma' works", {
+  SW(fit_gamma_log <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
                                W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
-                              family = poisson(link = "log"),
-                              W = W, iter = ITER, chains = CHAINS))
-    preds_gauss <- posterior_predict(fit_gauss)
-    preds_binom <- posterior_predict(fit_binom)
-    preds_pois <- posterior_predict(fit_pois)
-  })
-  
-  test_that("predict works for stan_besag", {
-    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    pred_besag <- predict(fit_besag)
-    pred_new_besag <- predict(fit_besag, newdata = data.frame(x = rnorm(100, 2, 1)))
-  })
-  
-  test_that("predict errors if nrow(newdata) < number of spatial regions", {
-    SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    pred_besag <- predict(fit_besag)
-    expect_error(pred_new_besag <- predict(fit_besag, newdata = data.frame(x = rnorm(10, 2, 1))),
-                 "'newdata' is less than the number of spatial regions.")
-  })
-  
-}
\ No newline at end of file
+  SW(fit_gamma_ident <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_gamma_sqrt <- stan_besag(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+                                W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_gamma_log)
+  expect_stanreg(fit_gamma_ident)
+  expect_stanreg(fit_gamma_sqrt)
+})
+
+# test QR 
+test_that("QR errors when number of predictors is <= 1", {
+  expect_error(
+    stan_besag(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
+    "'QR' can only be specified when there are multiple predictors"
+  )
+})
+
+test_that("QR works when number of x predictors is > 1", {
+  SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                                   W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+  expect_stanreg(fit_besag)
+})
+
+test_that("stan_besag errors with algorithm = 'optimizing'", {
+  expect_error(stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
+                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+               "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
+})
+
+test_that("loo/waic for stan_besag works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
+
+test_that("posterior_predict works for stan_besag", {
+  preds_gauss <- posterior_predict(fit_gauss)
+  preds_binom <- posterior_predict(fit_binom)
+  preds_pois <- posterior_predict(fit_pois)
+  preds_nb2 <- posterior_predict(fit_nb2)
+  preds_gamma <- posterior_predict(fit_gamma)
+})
+
+test_that("predict works for stan_besag", {
+  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  predict(fit_gauss)
+  predict(fit_binom)
+  predict(fit_pois)
+  predict(fit_nb2)
+  predict(fit_gamma)
+  predict(fit_gauss, newdata = new_dat)
+  predict(fit_binom, newdata = new_dat)
+  predict(fit_pois, newdata = new_dat)
+  predict(fit_nb2, newdata = new_dat)
+  predict(fit_gamma, newdata = new_dat)
+})
+
+test_that("predict errors if nrow(newdata) < number of spatial regions", {
+  expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
+               "'newdata' is less than the number of spatial regions.")
+})
+

From b846069d85f41cf3f720a5885ee7ddf11ec1fc40 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 13 Aug 2017 02:26:08 -0400
Subject: [PATCH 27/59] changing stan_bym to stan_bym2 (INLA convention)

---
 NAMESPACE                        |  2 +-
 R/{stan_bym.R => stan_bym2.R}    | 14 +++++++-------
 R/stan_spatial.fit.R             | 10 +++++-----
 tests/testthat/test_stan_besag.R | 15 ++++++++++-----
 4 files changed, 23 insertions(+), 18 deletions(-)
 rename R/{stan_bym.R => stan_bym2.R} (95%)

diff --git a/NAMESPACE b/NAMESPACE
index 822a103a2..7bb6ead47 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -103,7 +103,7 @@ export(stan_betareg)
 export(stan_betareg.fit)
 export(stan_biglm)
 export(stan_biglm.fit)
-export(stan_bym)
+export(stan_bym2)
 export(stan_gamm4)
 export(stan_glm)
 export(stan_glm.fit)
diff --git a/R/stan_bym.R b/R/stan_bym2.R
similarity index 95%
rename from R/stan_bym.R
rename to R/stan_bym2.R
index 868da82b2..03fa3f8f9 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym2.R
@@ -21,7 +21,7 @@
 #' 
 #' @export
 #' 
-#' @templateVar fun stan_bym
+#' @templateVar fun stan_bym2
 #' @templateVar fitfun stan_spatial.fit
 #' @templateVar pkg rstanarm
 #' @templateVar pkgfun stan_glm
@@ -53,16 +53,16 @@
 #' @param order Order of the spatial random walk. Specifying \code{order = 2}
 #'   will smooth the spatial variation. The default is \code{order = 1}.
 #'   
-#' @details The \code{stan_bym} model is similar to the BYM2 model in R-INLA.
+#' @details The \code{stan_bym2} model is similar to the BYM2 model in R-INLA.
 #'   However, instead of using the integrated nested Laplace approximation 
 #'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm} 
 #'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
 #'   regression coefficients, spatial mixing parameter, overall spatial
-#'   variation, and any applicable auxiliary parameters. The \code{stan_bym}
+#'   variation, and any applicable auxiliary parameters. The \code{stan_bym2}
 #'   function calls the workhorse \code{stan_spatial.fit} function, but it is
 #'   also possible to call the latter directly.
 #' 
-#' @seealso The vignette for \code{stan_bym}.
+#' @seealso The vignette for \code{stan_bym2}.
 #' 
 #' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An
 #'   intuitive Bayesian spatial model for disease mapping that accounts for
@@ -102,9 +102,9 @@
 #' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
 #' 
 #' # fit the model
-#' fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' fit_bym <- stan_bym2(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
 #' pp_check(fit_besag)
-stan_bym <- function(formula,
+stan_bym2 <- function(formula,
                         family = gaussian(),
                         data,
                         trials = NULL,
@@ -117,7 +117,7 @@ stan_bym <- function(formula,
                         algorithm = c("sampling", "meanfield", "fullrank"),
                         adapt_delta = NULL,
                         QR = FALSE) {
-  stan_function <- "stan_bym"
+  stan_function <- "stan_bym2"
   if (!requireNamespace("INLA", quietly = TRUE))
     stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 9c8bd2463..70baa6fab 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -17,7 +17,7 @@
 
 #' Workhorse function for CAR models.
 #' 
-#' Both \code{stan_besag} and \code{stan_bym} call \code{stan_spatial.fit} to
+#' Both \code{stan_besag} and \code{stan_bym2} call \code{stan_spatial.fit} to
 #' fit the appropriate spatial model. See the documentation for either modeling
 #' function for further details on the arguments of \code{stan_spatial.fit}.
 #' 
@@ -25,7 +25,7 @@
 #' 
 
 stan_spatial.fit <- function(x, y, w,
-                             stan_function = c("stan_besag", "stan_bym"),
+                             stan_function = c("stan_besag", "stan_bym2"),
                              family = NULL,
                              trials = NULL,
                              order = c(1,2),
@@ -86,7 +86,7 @@ stan_spatial.fit <- function(x, y, w,
   
   if (stan_function == "stan_besag")
     model_type <- 1
-  else if (stan_function == "stan_bym")
+  else if (stan_function == "stan_bym2")
     model_type <- 2
   
   if (!(order %in% c(1,2)))
@@ -126,7 +126,7 @@ stan_spatial.fit <- function(x, y, w,
     assign(i, prior_tau_stuff[[i]])
   
   # Deal with prior_rho
-  if (stan_function == "stan_bym") {
+  if (stan_function == "stan_bym2") {
     has_rho <- 1
     if (is.null(prior_rho)) {
       prior_dist_for_rho <- 0
@@ -234,7 +234,7 @@ stan_spatial.fit <- function(x, y, w,
                     num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0),
                     prior_dist_rho = prior_dist_for_rho)
   
-  if (stan_function == "stan_bym")
+  if (stan_function == "stan_bym2")
     standata$scaling_factor <- create_scaling_factor(standata)
   else
     standata$scaling_factor <- 0
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 3f380a567..51588ae2c 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -38,19 +38,24 @@ sp2weightmatrix <- function(spatialpolygon) {
 W <- sp2weightmatrix(grid_sim15)
 spatial_data <- grid_sim15@data
 
-SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
+SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                            W = W, iter = 100, chains = 4))
 SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                           prior_intercept = normal(0,1), prior = normal(0,1),
                            family = binomial(link = "logit"),
                            W = W, iter = 100, chains = 4))
 SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
+                          prior_intercept = normal(0,1), prior = normal(0,1),
                           family = poisson(link = "log"),
                           W = W, iter = 100, chains = 4))
 SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
-                          family = neg_binomial_2(link = "log"),
-                          W = W, iter = 100, chains = 4))
+                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                         family = neg_binomial_2(link = "log"),
+                         W = W, iter = 100, chains = 4))
 SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-                                 W = W, iter = 100, chains = 4))
+                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                           W = W, iter = 100, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
 test_that("stan_besag estimates match INLA", {
@@ -73,7 +78,7 @@ test_that("stan_besag estimates match INLA", {
 
 # test family/link combinations
 test_that("family = 'gaussian' works", {
-  SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
+  SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                                    W = W, iter = ITER, chains = CHAINS))
   SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
                                  W = W, iter = ITER, chains = CHAINS))

From 9aafe0be1e1ac6c982d6e3bc89038b1e1b48271b Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 13 Aug 2017 21:08:45 -0400
Subject: [PATCH 28/59] adding BYM model; changing spatial parameter names

---
 NAMESPACE            |   1 +
 R/stan_bym.R         | 153 +++++++++++++++++++++++++++++++++++++++++++
 R/stan_bym2.R        |   5 +-
 R/stan_spatial.fit.R |  60 ++++++++++++-----
 exec/spatial.stan    |  26 ++++++--
 5 files changed, 222 insertions(+), 23 deletions(-)
 create mode 100644 R/stan_bym.R

diff --git a/NAMESPACE b/NAMESPACE
index 7bb6ead47..c4eeea96e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -103,6 +103,7 @@ export(stan_betareg)
 export(stan_betareg.fit)
 export(stan_biglm)
 export(stan_biglm.fit)
+export(stan_bym)
 export(stan_bym2)
 export(stan_gamm4)
 export(stan_glm)
diff --git a/R/stan_bym.R b/R/stan_bym.R
new file mode 100644
index 000000000..f7eca5df5
--- /dev/null
+++ b/R/stan_bym.R
@@ -0,0 +1,153 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2013, 2014, 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+#' Bayesian spatial CAR BYM models via Stan
+#'
+#' Spatial regression modeling with a variant of the Besag, York, Mollie (BYM)
+#' conditional autoregressive (CAR) prior.
+#' 
+#' @export
+#' 
+#' @templateVar fun stan_bym
+#' @templateVar fitfun stan_spatial.fit
+#' @templateVar pkg rstanarm
+#' @templateVar pkgfun stan_glm
+#' @templateVar sameargs family 
+#' @template return-stanreg-object
+#' @template return-stanfit-object
+#' @template args-formula-data-subset
+#' @template args-same-as
+#' @template args-x-y
+#' @template args-dots
+#' @template args-prior_intercept
+#' @template args-priors
+#' @template args-prior_PD
+#' @template args-algorithm
+#' @template args-adapt_delta
+#' @template args-QR
+#' 
+#' @param trials If \code{family = binomial()} then a vector of trials (equal in
+#'   length to the outcome) must be declared.
+#' @param W An N-by-N spatial weight matrix.
+#' @param prior_tau The prior on the marginal variance contribution of the
+#'   unstructured (random) effect.
+#' @param prior_rho The prior on the marginal variance contribution of the
+#'   structured (spatial) effect.
+#' @param order Order of the spatial random walk. Specifying \code{order = 2}
+#'   will smooth the spatial variation. The default is \code{order = 1}.
+#'   
+#' @details The \code{stan_bym} model is similar to the BYM model in R-INLA.
+#'   However, instead of using the integrated nested Laplace approximation 
+#'   (INLA) method, full Bayesian estimation is performed (if \code{algorithm} 
+#'   is \code{"sampling"}) via MCMC. The model includes priors on the intercept,
+#'   regression coefficients, spatial mixing parameter, overall spatial
+#'   variation, and any applicable auxiliary parameters. The \code{stan_bym}
+#'   function calls the workhorse \code{stan_spatial.fit} function, but it is
+#'   also possible to call the latter directly.
+#' 
+#' @seealso The vignette for \code{stan_bym}.
+#' 
+#' @references Riebler, A., Sorbye, S.H., Simpson, D., Rue, H. (2016). An
+#'   intuitive Bayesian spatial model for disease mapping that accounts for
+#'   scaling. arXiv preprint	arXiv:1601.01180.
+#'   
+#'   Besag, J., York, J. and Mollié, A. (1991). Bayesian image restoration, with
+#'   two applications in spatial statistics. Annals of the Institute of
+#'   Statistical Mathematics. Vol. 43, No. 01, p1-20.
+#' 
+#' @examples 
+#' ### Simulated Data on a Lattice
+#' 
+#' data("lattice", package = "rstanarm")
+#' 
+#' # plot GMRF
+#' grid_sim <- grid_sim15
+#' var_range_gmrf <- seq(min(grid_sim@data$gmrf), max(grid_sim@data$gmrf), length = 50)
+#' spplot(grid_sim, "gmrf", at = var_range_gmrf, main = expression(paste(phi, " (GMRF)")),
+#'        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(50))
+#' 
+#' # Convert a spatial polygon to an N-by-N weight matrix
+#' sp2weightmatrix <- function(spatialpolygon) {
+#'   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+#' }
+#' 
+#' # convert spatial object to neighborhood matrix
+#' W <- sp2weightmatrix(grid_sim)
+#' # W_sparse <- Matrix(W, sparse = TRUE, dimnames = list(NULL,NULL)) 
+#' 
+#' # simulate predictor/outcome
+#' x <- rnorm(nrow(W), 3, 1)
+#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
+#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' 
+#' # fit the model
+#' fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' pp_check(fit_besag)
+stan_bym <- function(formula,
+                      family = gaussian(),
+                      data,
+                      trials = NULL,
+                      W,
+                      order = 1,
+                      ...,
+                      prior = normal(), prior_intercept = normal(),
+                      prior_tau = normal(), prior_rho = normal(), prior_aux = NULL,
+                      prior_PD = FALSE,
+                      algorithm = c("sampling", "meanfield", "fullrank"),
+                      adapt_delta = NULL,
+                      QR = FALSE) {
+  stan_function <- "stan_bym"
+  if (!requireNamespace("INLA", quietly = TRUE))
+    stop(paste("Please install the INLA package before using", stan_function))
+  mc <- match.call(expand.dots = FALSE)
+  algorithm <- match.arg(algorithm)
+  family <- validate_family(family)
+  mf <- model.frame(mc, data)
+  mt <- terms(formula, data = data)
+  Y <- array1D_check(model.response(mf, type = "any"))
+  X <- model.matrix(formula, data)
+  
+  stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
+                              trials = trials,
+                              family = family,
+                              stan_function = stan_function,
+                              order = order,
+                              ...,
+                              prior = prior,
+                              prior_intercept = prior_intercept,
+                              prior_aux = prior_aux, prior_rho = prior_rho, prior_tau = prior_tau,
+                              prior_PD = prior_PD,
+                              algorithm = algorithm, adapt_delta = adapt_delta, 
+                              QR = QR)
+  fit <- nlist(stanfit,
+               algorithm,
+               data,
+               x = X, y = Y,
+               family,
+               formula,
+               model = mf, 
+               terms = mt,
+               call = match.call(),
+               stan_function = stan_function)
+  
+  if (family$family == "binomial") {
+    fit$family <- binomial(link = "logit")
+    fit$trials <- trials
+  }
+  out <- stanreg(fit)
+  structure(out, class = c("stanreg", "car"))
+}
diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index 03fa3f8f9..91c836cd0 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -15,9 +15,10 @@
 # along with this program; if not, write to the Free Software
 # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
-#' Bayesian spatial CAR BYM models via Stan
+#' Bayesian spatial CAR BYM variant models via Stan
 #'
-#' Spatial regression modeling with a variant of the Besag, York, Mollie (BYM) conditional autoregressive (CAR) prior.
+#' Spatial regression modeling with a variant of the Besag, York, Mollie (BYM)
+#' conditional autoregressive (CAR) prior that accounts for scaling.
 #' 
 #' @export
 #' 
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 70baa6fab..99f464780 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -74,7 +74,7 @@ stan_spatial.fit <- function(x, y, w,
   else
     has_aux <- TRUE
   
-  if (family != "binomial")
+  if (family$family != "binomial")
     trials <- array(0, dim = c(0))
   
   if (family$family %in% c("binomial", "poisson", "neg_binomial_2")) {
@@ -86,8 +86,10 @@ stan_spatial.fit <- function(x, y, w,
   
   if (stan_function == "stan_besag")
     model_type <- 1
-  else if (stan_function == "stan_bym2")
+  else if (stan_function == "stan_bym")
     model_type <- 2
+  else if (stan_function == "stan_bym2")
+    model_type <- 3
   
   if (!(order %in% c(1,2)))
     stop("Argument 'order' must be 1 or 2.")
@@ -138,22 +140,44 @@ stan_spatial.fit <- function(x, y, w,
       prior_dist_for_rho <- 1
       prior_dist_name_for_rho <- "beta"
     }
-    prior_rho_stuff <- list(prior_dist_name_for_rho = prior_dist_name_for_rho,
-                            shape1 = prior_rho$alpha,
-                            shape2 = prior_rho$beta,
-                            prior_mean_for_rho = 0,
-                            prior_scale_for_rho = 0,
-                            prior_df_for_rho = 0)
+    prior_rho_stuff <- handle_glm_prior(NULL, nvars = 1, family$link, default_scale = 1, 
+                                        ok_dists = ok_scale_dists)
+    names(prior_rho_stuff) <- paste0(names(prior_rho_stuff), 
+                                     "_for_rho")
+    for (i in names(prior_rho_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_rho_stuff[[i]])
+    prior_rho_stuff$shape1 <- prior_rho$alpha
+    prior_rho_stuff$shape2 <- prior_rho$beta
   }
   else if (stan_function == "stan_besag") {
     has_rho <- 0
     prior_dist_for_rho <- 0
-    prior_rho_stuff <- list(prior_dist_name_for_rho = NA)
-    prior_scale_for_rho <- 0
+    # prior_rho_stuff <- list(prior_dist_name_for_rho = NA)
+    # prior_scale_for_rho <- 0
+    # prior_rho_stuff$shape1 <- 0
+    # prior_rho_stuff$shape2 <- 0
+    prior_rho_stuff <- handle_glm_prior(NULL, nvars = 1, family$link, default_scale = 1, 
+                                        ok_dists = ok_scale_dists)
+    names(prior_rho_stuff) <- paste0(names(prior_rho_stuff), 
+                                     "_for_rho")
+    for (i in names(prior_rho_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_rho_stuff[[i]])
     prior_rho_stuff$shape1 <- 0
     prior_rho_stuff$shape2 <- 0
   }
-
+  else if (stan_function == "stan_bym") {
+    has_rho <- 1
+    prior_rho_stuff <- handle_glm_prior(prior_rho, nvars = 1, family$link, default_scale = 1, 
+                                        ok_dists = ok_scale_dists)
+    names(prior_rho_stuff) <- paste0(names(prior_rho_stuff), 
+                                     "_for_rho")
+    for (i in names(prior_rho_stuff)) # prior_{dist, mean, scale, df, autoscale}
+      assign(i, prior_rho_stuff[[i]])
+    prior_rho_stuff$shape1 <- 0
+    prior_rho_stuff$shape2 <- 0
+  }
+  
+  # deal with auxiliary parameter
   if (has_aux) {
     prior_aux_stuff <- handle_glm_prior(prior_aux, nvars = 1, family$link, default_scale = 1, 
                                     ok_dists = ok_dists)
@@ -211,6 +235,7 @@ stan_spatial.fit <- function(x, y, w,
                     shape2_rho = c(prior_rho_stuff$shape2),
                     prior_dist_for_intercept = prior_dist_for_intercept,
                     prior_dist = prior_dist,
+                    prior_dist_rho = prior_dist_for_rho,
                     prior_dist_tau = prior_dist_for_tau,
                     prior_dist_for_aux = prior_dist_for_aux,
                     prior_mean_for_intercept = c(prior_mean_for_intercept),
@@ -219,6 +244,9 @@ stan_spatial.fit <- function(x, y, w,
                     prior_mean = as.array(prior_mean),
                     prior_scale = as.array(prior_scale),
                     prior_df = as.array(prior_df),
+                    prior_mean_rho = c(prior_mean_for_rho),
+                    prior_scale_rho = c(prior_scale_for_rho),
+                    prior_df_rho = c(prior_df_for_rho),
                     prior_mean_tau = c(prior_mean_for_tau),
                     prior_scale_tau = c(prior_scale_for_tau),
                     prior_df_tau = c(prior_df_for_tau),
@@ -231,8 +259,7 @@ stan_spatial.fit <- function(x, y, w,
                     global_prior_df_for_intercept,
                     global_prior_scale,
                     global_prior_scale_for_intercept,
-                    num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0),
-                    prior_dist_rho = prior_dist_for_rho)
+                    num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0))
   
   if (stan_function == "stan_bym2")
     standata$scaling_factor <- create_scaling_factor(standata)
@@ -256,7 +283,7 @@ stan_spatial.fit <- function(x, y, w,
     standata$u <- array(0, dim = c(0))
   }
   
-  pars <- c(if (has_intercept) "alpha", "beta", if(model_type == 2) "rho", "tau", if(has_aux) "aux",
+  pars <- c(if (has_intercept) "alpha", "beta", if(model_type != 1) "rho", "tau", if(has_aux) "aux",
             "mean_PPD", "psi")
   
   switch_aux <- switch(family$family,
@@ -339,7 +366,10 @@ stan_spatial.fit <- function(x, y, w,
       } 
     }
     new_names <- c(if (has_intercept) "(Intercept)", 
-                   colnames(xtemp), if(model_type == 2) "rho", "tau",
+                   colnames(xtemp),
+                   if(model_type == 1) {"structured"},  # tau
+                   if(model_type == 2) {c("structured", "unstructured")},  # rho, tau
+                   if(model_type == 3) {c("mixing", "structured")},  # rho, tau
                    if(has_aux) switch_aux, "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
 
     stanfit@sim$fnames_oi <- new_names
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 4d2e105b9..ea4678ca1 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -46,7 +46,7 @@ data {
   int link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
-  int<lower=1,upper=2> model_type;       // 0 = 1D RW; Besag = 1; BYM = 2
+  int<lower=1,upper=3> model_type;       // 0 = 1D RW; Besag = 1; BYM = 2
   int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
   int<lower=0> trials[family == 4 ? N : 0];
@@ -63,6 +63,9 @@ data {
   int u[order == 2 ? N+1 : 0];
   // prior stuff
   int<lower=0,upper=1> prior_dist_rho;
+  real prior_mean_rho;
+  real<lower=0> prior_scale_rho;
+  real<lower=0> prior_df_rho;
   real<lower=0> shape1_rho;
   real<lower=0> shape2_rho;
   real scaling_factor;
@@ -98,9 +101,9 @@ transformed data {
 parameters {
   real<lower=make_lower(family, link), upper=make_upper(family,link)> gamma[has_intercept];  // raw intercept
   vector[K] z_beta;  // standard normal term
-  vector[model_type == 2? N : 0] theta_raw;        // used for random effect (non-spatial)
+  vector[model_type == 1? 0 : N] theta_raw;        // used for random effect (non-spatial)
   vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0,upper=1> rho[model_type == 2];          // variance i.e. rho^2
+  real<lower=0,upper=1> rho[model_type != 1];          // variance i.e. rho^2
   real<lower=0> tau;        // variance i.e. tau^2
   real<lower=0> global[hs];
   vector<lower=0>[K] local[hs];
@@ -121,7 +124,9 @@ transformed parameters {
   if (model_type == 1)
     psi = phi * sqrt(inv(tau));
   else if (model_type == 2)
-    psi = tau*(sqrt(1-rho[1])*theta_raw[1] + sqrt(rho[1]/scaling_factor)*phi);
+    psi = phi * sqrt(inv(rho[1])) + theta_raw * sqrt(inv(tau));
+  else if (model_type == 3)
+    psi = tau*(sqrt(1-rho[1])*theta_raw + sqrt(rho[1]/scaling_factor)*phi);
   // for regression coefficients
   #include "tparameters.stan"
 }
@@ -160,8 +165,17 @@ model {
   // priors on coefficients
   #include "priors.stan"
   // model specific priors
-  if (model_type == 2) {  // BYM
-    target+= normal_lpdf(theta_raw[1] | 0, 1);  // unstructured (random) effect
+  if (model_type == 2) {
+    // prior on overall spatial variation
+    if (prior_dist_rho == 1)
+      target+= normal_lpdf(rho | prior_mean_rho, prior_scale_rho);
+    else if (prior_dist_rho == 2)
+      target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
+    else if (prior_dist_rho == 3)
+      target+= exponential_lpdf(rho | prior_scale_rho);
+  }
+  else if (model_type == 3) {  // BYM
+    target+= normal_lpdf(theta_raw | 0, 1);  // unstructured (random) effect
     if (prior_dist_rho == 1)
       target+= beta_lpdf(rho[1] | shape1_rho, shape2_rho);
     /* else prior_dist_tau is 0 and nothing is added */

From 3582ca6b148a5a37fa1b33b0f4fdf9539592031d Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 14 Aug 2017 13:56:50 -0400
Subject: [PATCH 29/59] change parameterization in spatial.stan from prec to sd

---
 R/stan_besag.R                   |  9 ++++++---
 R/stan_bym.R                     | 12 +++++++++---
 R/stan_bym2.R                    | 12 +++++++++---
 R/stan_spatial.fit.R             |  1 -
 exec/spatial.stan                | 24 +++++++++++++++++-------
 tests/testthat/test_stan_besag.R |  9 +++++----
 6 files changed, 46 insertions(+), 21 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 8d3a8214a..23e800b31 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -89,11 +89,14 @@
 #' 
 #' # simulate predictor/outcome
 #' x <- rnorm(nrow(W), 3, 1)
-#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
-#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' phi <- grid_sim@data$gmrf
+#' tau <- 1
+#' y <- rnorm(nrow(W), 0 + 0.4*x + phi*tau, 1)
 #' 
 #' # fit the model
-#' fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = data.frame(y=y,x=x),
+#'     W = W, iter = 300, chains = 4)
+#' fit_besag
 #' pp_check(fit_besag)
 #'
 stan_besag <- function(formula,
diff --git a/R/stan_bym.R b/R/stan_bym.R
index f7eca5df5..923bd9083 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -91,11 +91,17 @@
 #' 
 #' # simulate predictor/outcome
 #' x <- rnorm(nrow(W), 3, 1)
-#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
-#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' phi <- grid_sim@data$gmrf
+#' theta <- rnorm(nrow(W), 0, 1)
+#' tau <- 1
+#' rho <- 1
+#' spatial_data <- data.frame(x)
+#' y <- rnorm(nrow(W), 0 + 0.4*x + phi*rho + theta*tau, 1)
 #' 
 #' # fit the model
-#' fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' fit_bym <- stan_bym(y ~ 1 + x +, data = data.frame(y=y,x=x),
+#'     W = W, iter = 300, chains = 4)
+#' fit_bym
 #' pp_check(fit_besag)
 stan_bym <- function(formula,
                       family = gaussian(),
diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index 91c836cd0..bb844195b 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -99,11 +99,17 @@
 #' 
 #' # simulate predictor/outcome
 #' x <- rnorm(nrow(W), 3, 1)
-#' spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
-#' spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
+#' phi <- grid_sim@data$gmrf
+#' theta <- rnorm(nrow(W), 0, 1)
+#' tau <- 1
+#' rho <- 0.7
+#' psi <- (1/tau) * (sqrt(1-rho)*theta + sqrt(rho)*phi)
+#' y <- rnorm(nrow(W), 0 + 0.4 * x + psi, 1)
 #' 
 #' # fit the model
-#' fit_bym <- stan_bym2(y_gauss ~ 1 + x + I(x^2), data = spatial_data, W = W, iter = 300, chains = 4)
+#' fit_bym2 <- stan_bym2(y ~ 1 + x, data = data.frame(y=y,x=x),
+#'     W = W, iter = 300, chains = 4)
+#' fit_bym2
 #' pp_check(fit_besag)
 stan_bym2 <- function(formula,
                         family = gaussian(),
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 99f464780..75bed8206 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -371,7 +371,6 @@ stan_spatial.fit <- function(x, y, w,
                    if(model_type == 2) {c("structured", "unstructured")},  # rho, tau
                    if(model_type == 3) {c("mixing", "structured")},  # rho, tau
                    if(has_aux) switch_aux, "mean_PPD", paste0("psi[", 1:standata$N, "]"), "log-posterior")
-
     stanfit@sim$fnames_oi <- new_names
     return(structure(stanfit, prior.info = prior_info))
   }
diff --git a/exec/spatial.stan b/exec/spatial.stan
index ea4678ca1..7bd51ccfa 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -46,7 +46,7 @@ data {
   int link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
-  int<lower=1,upper=3> model_type;       // 0 = 1D RW; Besag = 1; BYM = 2
+  int<lower=1,upper=3> model_type;       // Besag = 0; BYM = 1; BYM = 2
   int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
   int<lower=0> trials[family == 4 ? N : 0];
@@ -99,12 +99,13 @@ transformed data {
   else hs = 0;
 }
 parameters {
-  real<lower=make_lower(family, link), upper=make_upper(family,link)> gamma[has_intercept];  // raw intercept
-  vector[K] z_beta;  // standard normal term
-  vector[model_type == 1? 0 : N] theta_raw;        // used for random effect (non-spatial)
-  vector[N-1] phi_raw;        // used for random effect (spatial)
-  real<lower=0,upper=1> rho[model_type != 1];          // variance i.e. rho^2
-  real<lower=0> tau;        // variance i.e. tau^2
+  real<lower=make_lower(family, link), upper=make_upper(family,link)> gamma[has_intercept];
+  vector[K] z_beta;
+  vector[model_type == 1? 0 : N] theta_raw;
+  vector[N-1] phi_raw;
+  // interpretation of rho and tau depends on model_type!
+  real<lower=0,upper=(model_type == 3 ? 1 : positive_infinity())> rho[model_type != 1];
+  real<lower=0> tau;
   real<lower=0> global[hs];
   vector<lower=0>[K] local[hs];
   vector<lower=0>[K] mix[prior_dist == 5 || prior_dist == 6];
@@ -121,12 +122,20 @@ transformed parameters {
   vector[N] psi;
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
+  /*
   if (model_type == 1)
     psi = phi * sqrt(inv(tau));
   else if (model_type == 2)
     psi = phi * sqrt(inv(rho[1])) + theta_raw * sqrt(inv(tau));
   else if (model_type == 3)
     psi = tau*(sqrt(1-rho[1])*theta_raw + sqrt(rho[1]/scaling_factor)*phi);
+  */
+  if (model_type == 1)
+     psi = phi * tau;
+  else if (model_type == 2)
+     psi = phi * rho[1] + theta_raw * tau;
+  else if (model_type == 3)
+     psi = tau*(sqrt(1-rho[1])*theta_raw + sqrt(rho[1]/scaling_factor)*phi);
   // for regression coefficients
   #include "tparameters.stan"
 }
@@ -173,6 +182,7 @@ model {
       target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
     else if (prior_dist_rho == 3)
       target+= exponential_lpdf(rho | prior_scale_rho);
+    /* else prior_dist_tau is 0 and nothing is added */
   }
   else if (model_type == 3) {  // BYM
     target+= normal_lpdf(theta_raw | 0, 1);  // unstructured (random) effect
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 51588ae2c..5cd1a5106 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -25,7 +25,8 @@ ITER <- 50
 CHAINS <- 2
 
 context("stan_besag")
-
+# source(paste0("tests/testthat/",(file.path("helpers", "expect_stanreg.R"))))
+# source(paste0("tests/testthat/",(file.path("helpers", "SW.R"))))
 source(file.path("helpers", "expect_stanreg.R"))
 source(file.path("helpers", "SW.R"))
 
@@ -48,14 +49,14 @@ SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data =
 SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           family = poisson(link = "log"),
-                          W = W, iter = 100, chains = 4))
+                          W = W, iter = 500, chains = 4))
 SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          family = neg_binomial_2(link = "log"),
-                         W = W, iter = 100, chains = 4))
+                         W = W, iter = 500, chains = 4))
 SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = W, iter = 100, chains = 4))
+                           W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
 test_that("stan_besag estimates match INLA", {

From 9cc2a37e69abb2a4e533e198257264a581921cd9 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 14 Aug 2017 16:14:02 -0400
Subject: [PATCH 30/59] convert spatial weight matrix to sparse if necessary

---
 R/stan_spatial.fit.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 75bed8206..4a03f18c2 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -37,6 +37,8 @@ stan_spatial.fit <- function(x, y, w,
                              adapt_delta = NULL,
                              QR = FALSE) {
   # convert W to a sparse matrix if not already sparse.
+  if(!is(w, "sparseMatrix"))
+    w <- Matrix(w, sparse = TRUE)
   
   algorithm <- match.arg(algorithm)
 

From 87ec6a964643d1cf0a0680b4a78222415d6c5f7f Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 15 Aug 2017 02:24:25 -0400
Subject: [PATCH 31/59] prior must be on theta_raw when model_type = 2

---
 R/stan_spatial.fit.R  | 12 +++++++-
 exec/spatial.stan     | 11 ++++----
 vignettes/spatial.Rmd | 65 ++++++++++++++++++++++++++++++++++---------
 3 files changed, 69 insertions(+), 19 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 4a03f18c2..65e62fa9f 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -60,6 +60,16 @@ stan_spatial.fit <- function(x, y, w,
                        binomial = 4,
                        Gamma = 5)
   
+  # for when consistent-family-numbers gets merged
+  # family_num <- switch(family$family,
+  #                      gaussian = 1,
+  #                      Gamma = 2,
+  #                      inv_gaussian = 3,
+  #                      beta = 4,
+  #                      binomial = 5,
+  #                      poisson = 6,
+  #                      neg_binomial_2 = 7)
+  
   if (family$family %in% c("gaussian", "Gamma")) {
     is_continuous <- TRUE
     y_real <- y
@@ -392,7 +402,7 @@ create_scaling_factor <- function(dat) {
   # Compute the diagonal elements of the covariance matrix subject to the 
   # constraint that the entries of the ICAR sum to zero.
   # See the function help for further details.
-  Q_inv <- inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
+  Q_inv <- INLA::inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
   
   # Compute the geometric mean of the variances, which are on the diagonal of Q.inv
   scaling_factor <- exp(mean(log(Matrix::diag(Q_inv))))
diff --git a/exec/spatial.stan b/exec/spatial.stan
index 7bd51ccfa..116d1c5ed 100644
--- a/exec/spatial.stan
+++ b/exec/spatial.stan
@@ -46,7 +46,7 @@ data {
   int link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
-  int<lower=1,upper=3> model_type;       // Besag = 0; BYM = 1; BYM = 2
+  int<lower=1,upper=3> model_type;       // Besag = 1; BYM = 2; BYM2 = 3
   int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
   int<lower=0> trials[family == 4 ? N : 0];
@@ -122,7 +122,7 @@ transformed parameters {
   vector[N] psi;
   phi[1:(N - 1)] = phi_raw;
   phi[N] = -sum(phi_raw);
-  /*
+  /* precision form
   if (model_type == 1)
     psi = phi * sqrt(inv(tau));
   else if (model_type == 2)
@@ -175,13 +175,14 @@ model {
   #include "priors.stan"
   // model specific priors
   if (model_type == 2) {
+    target+= normal_lpdf(theta_raw | 0, 1);  // unstructured (random) effect
     // prior on overall spatial variation
     if (prior_dist_rho == 1)
-      target+= normal_lpdf(rho | prior_mean_rho, prior_scale_rho);
+      target+= normal_lpdf(rho[1] | prior_mean_rho, prior_scale_rho);
     else if (prior_dist_rho == 2)
-      target+= student_t_lpdf(rho | prior_df_rho, prior_mean_rho, prior_scale_rho);
+      target+= student_t_lpdf(rho[1] | prior_df_rho, prior_mean_rho, prior_scale_rho);
     else if (prior_dist_rho == 3)
-      target+= exponential_lpdf(rho | prior_scale_rho);
+      target+= exponential_lpdf(rho[1] | prior_scale_rho);
     /* else prior_dist_tau is 0 and nothing is added */
   }
   else if (model_type == 3) {  // BYM
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 5d758597d..46e21909f 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -60,7 +60,7 @@ Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\
 
 ## Besag (ICAR) Spatial Prior
 
-The `stan_besag` function fits the data to an ICAR model. This means that the spatial effect enters the linear predictor as
+The `stan_besag` modeling function fits the data to an ICAR model. This means that the spatial effect enters the linear predictor as
 $$
 \begin{align}
 \boldsymbol{\psi} = \boldsymbol{\phi} \\
@@ -71,19 +71,35 @@ $$
 
 where $\rho$ is a scalar that controls the overall spatial variation and has an appropriate hyperprior distribution. 
 
-## Variant of the BYM Spatial Prior
+## BYM
 
-The `stan_bym` model fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and thex unstructured (random) effect,
+The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The BYM model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial component takes the following form,
 $$
 \begin{align}
-\boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
+\boldsymbol{\psi} &= \rho\phi + \tau\theta \\
 f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
 \exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\mathbf{Q}\boldsymbol{\phi} \bigg) \\
-f(\theta_i) &= (2\pi\sigma^2)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
+f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
 \end{align}
 $$
 Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard normal.
 
+## BYM2 (Variant of the BYM Spatial Prior)
+
+The `stan_bym2` modeling function fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and thex unstructured (random) effect,
+$$
+\begin{align}
+\boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
+f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
+\exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\mathbf{Q}\boldsymbol{\phi} \bigg) \\
+f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
+\end{align}
+$$
+
+As in the BYM model $\boldsymbol{\theta}$ is distributed standard normal. Here, the parameter $\rho$ is on the unit interval and is interpreted as the proportion of spatial variation that is contributed to overall variation, and $\tau$ explains the overall (convolved) variation.
+
+Priors on $\rho$ should be chosen wisely as $\rho=0$ reduces to a model that not account for spatial variation and $\rho=1$ reduces to the ICAR model, which does not account for random variation among the spatial units. 
+
 ## Posterior
 
 Combining these components yields the following posteriors. For `stan_besag` we have,
@@ -96,7 +112,8 @@ f(\rho) \times
 f(\lambda)
 $$
 
-and for `stan_bym` we have,
+
+and for both `stan_bym` and `stan_bym2` we have,
 $$
 f(\alpha, \boldsymbol{\beta},\boldsymbol{\phi}, \boldsymbol{\theta}, \rho, \tau, \gamma | \mathbf{y},\mathbf{X}, \mathbf{Q}) \propto 
 \prod_{i=1}^N f(y_i | \gamma) \times 
@@ -112,7 +129,7 @@ $$
 
 As an example we use spatial units defined on a lattice. Below we plot a GMRF of 100 spatial units available in the rstanarm package.
 
-```{r load-lattice, fig.align='center', fig.height=5}
+```{r load-lattice, fig.align='center', fig.height=8}
 library(spdep)
 # Load the preconstruced lattice/GMRF
 data("lattice", package = "rstanarm")
@@ -155,11 +172,11 @@ Now we can fit the model with `stan_besag`.
 
 ```{r fit-besag, results = "hide"}
 library(rstanarm)
-fit_besag <- stan_besag(y ~ 1 + x + z, data = spatial_data,
+fit_besag <- stan_besag(y ~ 1 + x + z, data = spatial_data, W,
                         prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
                         family = binomial(link="logit"), trials = spatial_data$trials,
                         chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
-fit_besag_bad <- stan_besag(y ~ 1 + x + I(x^2), data = spatial_data,
+fit_besag_bad <- stan_besag(y ~ 1 + x + I(x^2), data = spatial_data, W,
                             prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
                             family = binomial(link="logit"), trials = spatial_data$trials,
                             chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
@@ -168,15 +185,15 @@ print(fit_besag_bad)
 ```
 
 As a two-dimensional posterior predictive check we can plot the posterior predictions on the lattice using the `posterior_predict` function.
-```{r ppcheck-2d, fig.align='center', fig.height=5}
+```{r ppcheck-2d, fig.align='center', fig.height=8}
 grid_sim@data$y_pred <- colMeans(posterior_predict(fit_besag))
 var_range_y_pred <- seq(min(grid_sim@data$y_pred), max(grid_sim@data$y_pred) + 1, by = 1)
-spplot(grid_sim, "y", at = var_range_y_pred, main = expression(y[pred]),
+spplot(grid_sim, "y_pred", at = var_range_y_pred, main = expression(y[pred]),
        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y_pred) + 1))
 ```
 
 Alternatively we can look at the conventional one-dimensional posterior predictive check with the `pp_check` function.
-```{r ppcheck-1d, fig.align='center', fig.height=5}
+```{r ppcheck-1d, fig.align='center', fig.height=8}
 pp_check(fit_besag)
 ```
 
@@ -196,7 +213,29 @@ compare(loo_besag, loo_besag_bad)
 
 ## Smoothing the Spatial Random Walk
 
-In some cases modeling the GMRF spatial component with the precsion matrix $\mathbf{Q}$ leads to rough spatial varation. This occurs when dealing with spatial units on a lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation. Below we visually illustrate this result with a random walk in one-dimension using the `stan_rw` function with the argument `smooth = TRUE`.
+In some cases modeling the GMRF spatial component with the precsion matrix $\mathbf{Q}$ leads to rough spatial varation. This occurs when dealing with spatial units on a lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation across the spatial units. Below we use the `order = 2` argument to fit the model with smoothing of order 2.
+
+```{r smoothing, results="hide"}
+fit_besag_smooth <- stan_besag(y ~ 1 + x + z, data = spatial_data, W, order = 2,
+                               prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
+                               family = binomial(link="logit"), trials = spatial_data$trials,
+                               chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
+```
+
+Now we plot the results alongside the first fit with `order = 1`.
+
+```{r plot-smoothing, fig.align='center', fig.height=12}
+grid_sim@data$y_pred_smooth <- colMeans(posterior_predict(fit_besag_smooth))
+
+var_range_y_pred <- seq(min(grid_sim@data$y_pred_smooth), max(grid_sim@data$y_pred_smooth) + 1, by = 1)
+gridExtra::grid.arrange(layout_matrix = matrix(1:2, nrow = 1),
+  spplot(grid_sim, "y_pred_smooth", at = var_range_y_pred, main = expression(y[pred_smooth]),
+         col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y_pred) + 1)),
+  spplot(grid_sim, "y_pred", at = var_range_y_pred, main = expression(y[pred]),
+         col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y_pred) + 1))
+)
+```
+
 
 ## References
 

From 4ea46745605e8ee1a777676521274df259079889 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 29 Aug 2017 23:49:53 -0400
Subject: [PATCH 32/59] remove INLA dependency for bym2 model

---
 R/stan_spatial.fit.R | 26 ++++++++++++++++++++------
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 65e62fa9f..a68080372 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -391,24 +391,38 @@ stan_spatial.fit <- function(x, y, w,
 # create scaling_factor a la Dan Simpson
 create_scaling_factor <- function(dat) {
   edges <- dat$edges
-  #Build the adjacency matrix
+  # Build the adjacency matrix
   adj.matrix <- Matrix::sparseMatrix(i=edges[,1],j=edges[,2],x=1,symmetric=TRUE)
-  #The ICAR precision matrix (note! This is singular)
+  # The ICAR precision matrix (note! This is singular)
   Q <-  Matrix::Diagonal(dat$N, Matrix::rowSums(adj.matrix)) - adj.matrix
-  
-  #Add a small jitter to the diagonal for numerical stability (optional but recommended)
+  # Add a small jitter to the diagonal for numerical stability (optional but recommended)
   Q_pert <- Q + Matrix::Diagonal(dat$N) * max(Matrix::diag(Q)) * sqrt(.Machine$double.eps)
-  
   # Compute the diagonal elements of the covariance matrix subject to the 
   # constraint that the entries of the ICAR sum to zero.
   # See the function help for further details.
-  Q_inv <- INLA::inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
+  # Q_inv <- INLA::inla.qinv(Q_pert, constr=list(A = matrix(1,1,dat$N),e=0))
+  Q_inv <- qinv(Q_pert, A = matrix(1,1,dat$N))
   
   # Compute the geometric mean of the variances, which are on the diagonal of Q.inv
   scaling_factor <- exp(mean(log(Matrix::diag(Q_inv))))
   return(scaling_factor)
 }
 
+# qinv function (analagous to inla.qinv)
+
+qinv <- function(Q, A = NULL) {
+  # need to replace the line below with the sparse version, using recursions
+  Sigma <- Matrix::solve(Q)
+  if (is.null(A))
+    return(Sigma)
+  else {
+    A <- matrix(1,1, nrow(Sigma))
+    W <- Sigma %*% t(A)
+    Sigma_const <- Sigma - W %*% solve(A %*% W) %*% t(W)
+    return(Sigma_const)
+  }
+}
+
 # Summarize spatial prior
 
 summarize_spatial_prior <- function(user_prior,

From 41555ff2119bb95e89fd5ad5a10a4ea4b343f517 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 30 Aug 2017 18:07:37 -0400
Subject: [PATCH 33/59] updating CAR spatial tests

---
 R/stan_bym2.R                    |   2 +-
 R/stanreg.R                      |   6 +-
 tests/testthat/test_stan_besag.R |  10 +-
 tests/testthat/test_stan_bym.R   | 317 ++++++++++++++++++-------------
 tests/testthat/test_stan_bym2.R  | 211 ++++++++++++++++++++
 5 files changed, 406 insertions(+), 140 deletions(-)
 create mode 100644 tests/testthat/test_stan_bym2.R

diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index bb844195b..56e7c41c3 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -162,7 +162,7 @@ stan_bym2 <- function(formula,
                terms = mt,
                call = match.call(),
                stan_function = stan_function)
-  
+
   if (family$family == "binomial") {
     fit$family <- binomial(link = "logit")
     fit$trials <- trials
diff --git a/R/stanreg.R b/R/stanreg.R
index ffee71f01..3911c1e08 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -31,7 +31,7 @@ stanreg <- function(object) {
   nobs <- NROW(y)
   ynames <- if (is.matrix(y)) rownames(y) else names(y)
   
-  is_car <- object$stan_function %in% c("stan_besag", "stan_bym")
+  is_car <- object$stan_function %in% c("stan_besag", "stan_bym", "stan_bym2")
   is_betareg <- is.beta(family$family)
   if (is_betareg) { 
     family_phi <- object$family_phi  # pull out phi family/link
@@ -45,7 +45,7 @@ stanreg <- function(object) {
   if (opt) {
     stanmat <- stanfit$theta_tilde
     probs <- c(0.025, .975)
-    browser()
+
     stan_summary <- cbind(Median = apply(stanmat, 2L, median), 
                           MAD_SD = apply(stanmat, 2L, mad),
                           t(apply(stanmat, 2L, quantile, probs)))
@@ -111,7 +111,7 @@ stanreg <- function(object) {
     eta_z <- linear_predictor(coefs_z, z, object$offset)
     phi <- family_phi$linkinv(eta_z)
   }
-  
+
   out <- nlist(
     coefficients = unpad_reTrms(coefs), 
     ses = unpad_reTrms(ses),
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 5cd1a5106..9f76dc376 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -60,11 +60,11 @@ SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
 test_that("stan_besag estimates match INLA", {
-  inla_gauss_est <- c(-0.0475, 0.4329, 0.9830, 0.8277)
-  inla_binom_est <- c(-1.9444, 0.3340, 0.423)
-  inla_pois_est <- c(0.7801, 0.4366, 0.3448)
-  inla_nb_est <- c(0.8053, 0.4362, 0.3574, 48.6003)
-  inla_gamma_est <- c(0.8119, -1.3407, 0.559, 1.106)
+  inla_gauss_est <- c(-0.0475, 0.4329, 1/0.9830, 1/0.8277)
+  inla_binom_est <- c(-1.9444, 0.3340, 1/0.4229)
+  inla_pois_est <- c(0.7801, 0.4366, 1/0.3448)
+  inla_nb_est <- c(0.8053, 0.4362, 1/0.3574, 48.6003)
+  inla_gamma_est <- c(0.8119, -1.3407, 1/0.559, 1/1.106)
   besag_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
   besag_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
   besag_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index 4320b4cb3..11a47c421 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -18,139 +18,194 @@
 # tests can be run using devtools::test() or manually by loading testthat 
 # package and then running the code below possibly with options(mc.cores = 4).
 
-if (.Platform$OS.type != "windows" && require(betareg)) {
-  library(rstanarm)
-  SEED <- 12345
-  set.seed(SEED)
-  ITER <- 50
-  CHAINS <- 2
-  
-  context("stan_bym")
-  
-  source(file.path("helpers", "expect_stanreg.R"))
-  source(file.path("helpers", "SW.R"))
-  
-  data("lattice10", package = "rstanarm")
-  
-  # Convert a spatial polygon to an N-by-N weight matrix
-  sp2weightmatrix <- function(spatialpolygon) {
-    spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
-  }
-  W <- sp2weightmatrix(grid_sim)
-  x <- rnorm(nrow(W), 2, 1)
-  spatial_data <- data.frame(x, phi = grid_sim@data$gmrf)
-  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
-  spatial_data$y_gauss <- rnorm(nrow(W), 0 + 0.4 * x + spatial_data$phi, 1)
-  spatial_data$y_pois <- rpois(nrow(W), exp(-1 + 1.4 * x + spatial_data$phi))
-  spatial_data$trials <- rep(10, nrow(W))
-  spatial_data$y_binom <- rbinom(nrow(W), spatial_data$trials, binomial(link = "logit")$linkinv(1 + 0.4 * x + spatial_data$phi))
-  
-  # test family/link combinations
-  test_that("family = 'gaussian' works", {
-    SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+library(rstanarm)
+SEED <- 12345
+set.seed(SEED)
+ITER <- 50
+CHAINS <- 2
+
+context("stan_bym")
+# source(paste0("tests/testthat/",(file.path("helpers", "expect_stanreg.R"))))
+# source(paste0("tests/testthat/",(file.path("helpers", "SW.R"))))
+source(file.path("helpers", "expect_stanreg.R"))
+source(file.path("helpers", "SW.R"))
+
+data("lattice", package = "rstanarm")
+
+# Convert a spatial polygon to an N-by-N weight matrix
+sp2weightmatrix <- function(spatialpolygon) {
+  spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+}
+W <- sp2weightmatrix(grid_sim15)
+spatial_data <- grid_sim15@data
+
+SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         W = W, iter = 100, chains = 4))
+SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                         prior_intercept = normal(0,1), prior = normal(0,1),
+                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         family = binomial(link = "logit"),
+                         W = W, iter = 100, chains = 4))
+SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
+                        prior_intercept = normal(0,1), prior = normal(0,1),
+                        prior_tau = normal(0,1), prior_rho = normal(0,1),
+                        family = poisson(link = "log"),
+                        W = W, iter = 500, chains = 4))
+SW(fit_nb2 <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
+                       prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                       prior_tau = normal(0,1), prior_rho = normal(0,1),
+                       family = neg_binomial_2(link = "log"),
+                       W = W, iter = 500, chains = 4))
+SW(fit_gamma <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         W = W, iter = 500, chains = 4))
+
+# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
+test_that("stan_bym estimates match INLA", {
+  inla_gauss_est <- c(-0.0469, 0.4327, 1/0.8603, 1/1.6159, 1/2.5314)
+  inla_binom_est <- c(-1.9449, 0.3339, 1/0.5624, 1/13.5187)
+  inla_pois_est <- c(0.7792, 0.4365, 1/0.3874, 1/52.7799)
+  inla_nb_est <- c(0.8008, 0.4366, 1/0.3806, 1/66.9760, 35.4347)
+  inla_gamma_est <- c(0.7881, -1.3443, 1/0.6516, 1/24.4265, 1/1.1556)
+  bym_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
+  bym_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
+  bym_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
+  bym_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
+  bym_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
+  expect_equal(bym_gauss, inla_gauss_est, tol = 0.2)
+  expect_equal(bym_binom, inla_binom_est, tol = 0.2)
+  expect_equal(bym_pois, inla_pois_est, tol = 0.2)
+  expect_equal(bym_nb2, inla_nb_est, tol = 0.2)
+  expect_equal(bym_gamma, inla_gamma_est, tol = 0.2)
+})
+
+# test family/link combinations
+test_that("family = 'gaussian' works", {
+  SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                                    W = W, iter = ITER, chains = CHAINS))
-    SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-                                   W = W, iter = 10, chains = CHAINS))
-    expect_stanreg(fit_gauss_ident)
-    expect_stanreg(fit_gauss_log)
-    expect_stanreg(fit_gauss_inv)
-  })
-  test_that("family = 'binomial' works", {
-    SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "logit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                      family = binomial(link = "probit"),
-                                      W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                       family = binomial(link = "cauchit"),
-                                       W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                   family = binomial(link = "log"),
+  SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_gauss_ident)
+  expect_stanreg(fit_gauss_log)
+  expect_stanreg(fit_gauss_inv)
+})
+test_that("family = 'binomial' works", {
+  SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                   family = binomial(link = "logit"),
                                    W = W, iter = ITER, chains = CHAINS))
-    SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                       family = binomial(link = "cloglog"),
-                                       W = W, iter = ITER, chains = CHAINS))
-    expect_stanreg(fit_binom_logit)
-    expect_stanreg(fit_binom_probit)
-    expect_stanreg(fit_binom_cauchit)
-    expect_stanreg(fit_binom_log)
-    expect_stanreg(fit_binom_cloglog)
-    
-  })
-  test_that("family = 'poisson' works", {
-    SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-                                  W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+  SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                    family = binomial(link = "probit"),
                                     W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-                                   W = W, iter = 10, chains = CHAINS))
-    expect_stanreg(fit_pois_log)
-    expect_stanreg(fit_pois_ident)
-    expect_stanreg(fit_pois_sqrt)
-  })
-  
-  # test QR 
-  test_that("QR errors when number of predictors is <= 1", {
-    expect_error(
-      stan_bym(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
-      "'QR' can only be specified when there are multiple predictors"
-    )
-  })
-  
-  test_that("QR works when number of x and/or z predictors is >= 1", {
-    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    expect_stanreg(fit_bym)
-  })
-  
-  test_that("stan_bym works with QR = TRUE and algorithm = 'optimizing'", {
-    
-  })
-  
-  test_that("loo/waic for stan_bym works", {
-    SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                               family = binomial(link = "logit"),
-                               W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
-                              family = poisson(link = "log"),
-                              W = W, iter = ITER, chains = CHAINS))
-    loo(fit_gauss)
-    loo(fit_binom)
-    loo(fit_pois)
-  })
+  SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cauchit"),
+                                     W = W, iter = ITER, chains = CHAINS))
+  SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                 family = binomial(link = "log"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cloglog"),
+                                     W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_binom_logit)
+  expect_stanreg(fit_binom_probit)
+  expect_stanreg(fit_binom_cauchit)
+  expect_stanreg(fit_binom_log)
+  expect_stanreg(fit_binom_cloglog)
   
-  test_that("posterior_predict works for stan_bym", {
-    SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                               family = binomial(link = "logit"),
+})
+test_that("family = 'poisson' works", {
+  SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+                                W = W, iter = ITER, chains = CHAINS))
+  SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+                                  W = W, iter = ITER, chains = CHAINS))
+  SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_pois_log)
+  expect_stanreg(fit_pois_ident)
+  expect_stanreg(fit_pois_sqrt)
+})
+
+# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+test_that("family = 'neg_binomial_2' works", {
+  SW(fit_nb2_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
                                W = W, iter = ITER, chains = CHAINS))
-    SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
-                              family = poisson(link = "log"),
-                              W = W, iter = ITER, chains = CHAINS))
-    preds_gauss <- posterior_predict(fit_gauss)
-    preds_binom <- posterior_predict(fit_binom)
-    preds_pois <- posterior_predict(fit_pois)
-  })
-  
-  test_that("predict works for stan_bym", {
-    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    pred_besag <- predict(fit_bym)
-    pred_new_besag <- predict(fit_bym, newdata = data.frame(x = rnorm(100, 2, 1)))
-  })
-  
-  test_that("predict errors if nrow(newdata) < number of spatial regions", {
-    SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                               W = W, iter = ITER, chains = CHAINS, QR = TRUE))
-    pred_besag <- predict(fit_bym)
-    expect_error(pred_new_besag <- predict(fit_bym, newdata = data.frame(x = rnorm(10, 2, 1))),
-                 "'newdata' is less than the number of spatial regions.")
-  })
-  
-}
\ No newline at end of file
+  SW(fit_nb2_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_nb2_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+                                W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_nb2_log)
+  expect_stanreg(fit_nb2_ident)
+  expect_stanreg(fit_nb2_sqrt)
+})
+
+test_that("family = 'Gamma' works", {
+  SW(fit_gamma_log <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
+                                 W = W, iter = ITER, chains = CHAINS))
+  SW(fit_gamma_ident <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                                   W = W, iter = ITER, chains = CHAINS))
+  SW(fit_gamma_sqrt <- stan_bym(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+                                  W = W, iter = ITER, chains = CHAINS))
+  expect_stanreg(fit_gamma_log)
+  expect_stanreg(fit_gamma_ident)
+  expect_stanreg(fit_gamma_sqrt)
+})
+
+# test QR 
+test_that("QR errors when number of predictors is <= 1", {
+  expect_error(
+    stan_bym(y_gauss ~ x, data = spatial_data, family = gaussian(), W = W, seed = SEED, QR = TRUE),
+    "'QR' can only be specified when there are multiple predictors"
+  )
+})
+
+test_that("QR works when number of x predictors is > 1", {
+  SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                             W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+  expect_stanreg(fit_bym)
+})
+
+test_that("stan_bym errors with algorithm = 'optimizing'", {
+  expect_error(stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
+                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+               "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
+})
+
+test_that("loo/waic for stan_bym works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
+
+test_that("posterior_predict works for stan_bym", {
+  preds_gauss <- posterior_predict(fit_gauss)
+  preds_binom <- posterior_predict(fit_binom)
+  preds_pois <- posterior_predict(fit_pois)
+  preds_nb2 <- posterior_predict(fit_nb2)
+  preds_gamma <- posterior_predict(fit_gamma)
+})
+
+test_that("predict works for stan_bym", {
+  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  predict(fit_gauss)
+  predict(fit_binom)
+  predict(fit_pois)
+  predict(fit_nb2)
+  predict(fit_gamma)
+  predict(fit_gauss, newdata = new_dat)
+  predict(fit_binom, newdata = new_dat)
+  predict(fit_pois, newdata = new_dat)
+  predict(fit_nb2, newdata = new_dat)
+  predict(fit_gamma, newdata = new_dat)
+})
+
+test_that("predict errors if nrow(newdata) < number of spatial regions", {
+  expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
+               "'newdata' is less than the number of spatial regions.")
+})
+
diff --git a/tests/testthat/test_stan_bym2.R b/tests/testthat/test_stan_bym2.R
new file mode 100644
index 000000000..3740c9318
--- /dev/null
+++ b/tests/testthat/test_stan_bym2.R
@@ -0,0 +1,211 @@
+# Part of the rstanarm package for estimating model parameters
+# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
+# 
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+# 
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+# tests can be run using devtools::test() or manually by loading testthat 
+# package and then running the code below possibly with options(mc.cores = 4).
+
+library(rstanarm)
+SEED <- 12345
+set.seed(SEED)
+ITER <- 50
+CHAINS <- 2
+
+context("stan_bym2")
+# source(paste0("tests/testthat/",(file.path("helpers", "expect_stanreg.R"))))
+# source(paste0("tests/testthat/",(file.path("helpers", "SW.R"))))
+source(file.path("helpers", "expect_stanreg.R"))
+source(file.path("helpers", "SW.R"))
+
+data("lattice", package = "rstanarm")
+
+# Convert a spatial polygon to an N-by-N weight matrix
+sp2weightmatrix <- function(spatialpolygon) {
+  spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
+}
+W <- sp2weightmatrix(grid_sim15)
+spatial_data <- grid_sim15@data
+
+SW(fit_gauss <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          W = W, iter = 100, chains = 4))
+SW(fit_binom <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                          prior_intercept = normal(0,1), prior = normal(0,1),
+                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          family = binomial(link = "logit"),
+                          W = W, iter = 100, chains = 4))
+SW(fit_pois <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
+                         prior_intercept = normal(0,1), prior = normal(0,1),
+                         prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                         family = poisson(link = "log"),
+                         W = W, iter = 500, chains = 4))
+SW(fit_nb2 <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
+                        prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                        prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                        family = neg_binomial_2(link = "log"),
+                        W = W, iter = 500, chains = 4))
+SW(fit_gamma <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
+                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          W = W, iter = 500, chains = 4))
+
+# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
+test_that("stan_bym2 estimates match INLA", {
+  inla_gauss_est <- c(-0.0499, 0.4337, 0.4048, 1/1.0845, 1/2.1234)
+  inla_binom_est <- c(-1.9432, 0.3338, 0.7963, 1/1.6385)
+  inla_pois_est <- c(0.7813, 0.4361, 0.9127, 1/1.3033)
+  inla_nb_est <- c(0.8017, 0.4363, 0.9136, 1/1.3051, 117.9266)
+  inla_gamma_est <- c(0.7968, -1.3430, 0.807, 1/1.909, 1/1.140)
+  bym2_gauss <- unname(fit_gauss$stan_summary[1:5,"mean"])
+  bym2_binom <- unname(fit_binom$stan_summary[1:4,"mean"])
+  bym2_pois <- unname(fit_pois$stan_summary[1:4,"mean"])
+  bym2_nb2 <- unname(fit_nb2$stan_summary[1:5,"mean"])
+  bym2_gamma <- unname(fit_gamma$stan_summary[1:5,"mean"])
+  expect_equal(bym2_gauss, inla_gauss_est, tol = 0.2)
+  expect_equal(bym2_binom, inla_binom_est, tol = 0.2)
+  expect_equal(bym2_pois, inla_pois_est, tol = 0.2)
+  expect_equal(bym2_nb2, inla_nb_est, tol = 0.2)
+  expect_equal(bym2_gamma, inla_gamma_est, tol = 0.2)
+})
+
+# test family/link combinations
+test_that("family = 'gaussian' works", {
+  SW(fit_gauss_ident <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+                                   W = W, iter = 10, chains = CHAINS))
+  SW(fit_gauss_log <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+                                 W = W, iter = 10, chains = CHAINS))
+  SW(fit_gauss_inv <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+                                 W = W, iter = 10, chains = CHAINS))
+  expect_stanreg(fit_gauss_ident)
+  expect_stanreg(fit_gauss_log)
+  expect_stanreg(fit_gauss_inv)
+})
+test_that("family = 'binomial' works", {
+  SW(fit_binom_logit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                   family = binomial(link = "logit"),
+                                   W = W, iter = 10, chains = CHAINS))
+  SW(fit_binom_probit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                    family = binomial(link = "probit"),
+                                    W = W, iter = 10, chains = CHAINS))
+  SW(fit_binom_cauchit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cauchit"),
+                                     W = W, iter = 10, chains = CHAINS))
+  SW(fit_binom_log <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                 family = binomial(link = "log"),
+                                 W = W, iter = 10, chains = CHAINS))
+  SW(fit_binom_cloglog <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+                                     family = binomial(link = "cloglog"),
+                                     W = W, iter = 10, chains = CHAINS))
+  expect_stanreg(fit_binom_logit)
+  expect_stanreg(fit_binom_probit)
+  expect_stanreg(fit_binom_cauchit)
+  expect_stanreg(fit_binom_log)
+  expect_stanreg(fit_binom_cloglog)
+  
+})
+test_that("family = 'poisson' works", {
+  SW(fit_pois_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+                                W = W, iter = 10, chains = CHAINS))
+  SW(fit_pois_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+                                  W = W, iter = 10, chains = CHAINS))
+  SW(fit_pois_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+                                 W = W, iter = 10, chains = CHAINS))
+  expect_stanreg(fit_pois_log)
+  expect_stanreg(fit_pois_ident)
+  expect_stanreg(fit_pois_sqrt)
+})
+
+# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+test_that("family = 'neg_binomial_2' works", {
+  SW(fit_nb2_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
+                               W = W, iter = 10, chains = CHAINS))
+  SW(fit_nb2_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+                                 W = W, iter = 10, chains = CHAINS))
+  SW(fit_nb2_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+                                W = W, iter = 10, chains = CHAINS))
+  expect_stanreg(fit_nb2_log)
+  expect_stanreg(fit_nb2_ident)
+  expect_stanreg(fit_nb2_sqrt)
+})
+
+test_that("family = 'Gamma' works", {
+  SW(fit_gamma_log <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
+                                 W = W, iter = 10, chains = CHAINS))
+  SW(fit_gamma_ident <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+                                   W = W, iter = 10, chains = CHAINS))
+  SW(fit_gamma_sqrt <- stan_bym2(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+                                  W = W, iter = 10, chains = CHAINS))
+  expect_stanreg(fit_gamma_log)
+  expect_stanreg(fit_gamma_ident)
+  expect_stanreg(fit_gamma_sqrt)
+})
+
+# test QR 
+test_that("QR errors when number of predictors is <= 1", {
+  expect_error(
+    stan_bym2(y_gauss ~ x, data = spatial_data, family = gaussian(), W = W, seed = SEED, QR = TRUE),
+    "'QR' can only be specified when there are multiple predictors"
+  )
+})
+
+test_that("QR works when number of x predictors is > 1", {
+  SW(stan_bym <- stan_bym2(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
+                             W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+  expect_stanreg(stan_bym)
+})
+
+test_that("stan_bym2 errors with algorithm = 'optimizing'", {
+  expect_error(stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
+                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+               "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
+})
+
+test_that("loo/waic for stan_bym2 works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
+
+test_that("posterior_predict works for stan_bym2", {
+  preds_gauss <- posterior_predict(fit_gauss)
+  preds_binom <- posterior_predict(fit_binom)
+  preds_pois <- posterior_predict(fit_pois)
+  preds_nb2 <- posterior_predict(fit_nb2)
+  preds_gamma <- posterior_predict(fit_gamma)
+})
+
+test_that("predict works for stan_bym2", {
+  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  predict(fit_gauss)
+  predict(fit_binom)
+  predict(fit_pois)
+  predict(fit_nb2)
+  predict(fit_gamma)
+  predict(fit_gauss, newdata = new_dat)
+  predict(fit_binom, newdata = new_dat)
+  predict(fit_pois, newdata = new_dat)
+  predict(fit_nb2, newdata = new_dat)
+  predict(fit_gamma, newdata = new_dat)
+})
+
+test_that("predict errors if nrow(newdata) < number of spatial regions", {
+  expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
+               "'newdata' is less than the number of spatial regions.")
+})
+

From 31e8d1cc6b449a70dc2c0f29dbcfca5b52d6dfeb Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 30 Aug 2017 18:44:03 -0400
Subject: [PATCH 34/59] update CAR vignette

---
 vignettes/spatial.Rmd | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 46e21909f..8a2e0c610 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -23,7 +23,7 @@ params:
 
 ## Introduction
 
-This vignette explains how to account for spatial variation with conditional autoregressive (CAR) models when modeling discrete or continuous outcomes using the `stan_besag` and `stan_bym` functions in the __rstanarm__ package. The `stan_besag` function allows you to model space as an intrinsic conditional autoregressive model (ICAR) and the `stan_bym` is a variant of the Besag, York, Mollie (YEAR) model where the priors are more interpretable.
+This vignette explains how to account for spatial variation with conditional autoregressive (CAR) models when modeling discrete or continuous outcomes using the `stan_besag`, `stan_bym`, and `stan_bym2` functions in the __rstanarm__ package. The `stan_besag` function allows you to model space as an intrinsic conditional autoregressive model (ICAR), the `stan_bym` function allows you to fit the Besag, York, Mollie (1991) CAR model, and the `stan_bym2` is a variant of the BYM model where the priors are more interpretable (see Riebler et al (2016) for details).
 
 ```{r, child="children/four_steps.txt"}
 ```
@@ -34,15 +34,15 @@ the likelihood is the product of beta distributions.
 
 ## Likelihood
 
-The likelihoods supported in each function include Gaussian, Binomial, Poisson, Negative Binomial, and Gamma. For details on the link functions available and the contribution to the posterior of each of these likelihoods consult the appropriate [vignette](http://mc-stan.org/rstanarm/articles/index.html). 
+The likelihoods supported in each CAR modeling function include Gaussian, Binomial, Poisson, Negative Binomial, and Gamma. For details on the link functions available and the contribution to the posterior of each of these likelihoods consult the appropriate [vignette](http://mc-stan.org/rstanarm/articles/index.html). 
 
 The linear predictor takes the following form,
 $$
 \boldsymbol{\eta} = \alpha + \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi}
 $$
-where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect (and $N$ denotes the number of spatial units). The construction of $\boldsymbol{\psi}$ depends on the model, and this is discussed in the relevant sections below.
+where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect (and $N$ denotes the number of spatial units). The construction of $\boldsymbol{\psi}$ depends on the model, which is discussed in the relevant sections below.
 
-Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). Thus, for some probability density/mass function $f$ the general form of the likelihood is,
+Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). Thus, for some probability density/mass function $f$ we can state the general form of the likelihood as,
 $$
 \mathcal{L}(\alpha, \boldsymbol{\beta}, \gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \alpha, \boldsymbol{\beta}, \gamma )
 $$
@@ -73,10 +73,10 @@ where $\rho$ is a scalar that controls the overall spatial variation and has an
 
 ## BYM
 
-The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The BYM model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial component takes the following form,
+The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The `stan_bym` model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial component takes the following form,
 $$
 \begin{align}
-\boldsymbol{\psi} &= \rho\phi + \tau\theta \\
+\boldsymbol{\psi} &= \rho\boldsymbol{\phi} + \tau\boldsymbol{\theta} \\
 f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
 \exp\bigg( -\frac{1}{2}\boldsymbol{\phi}^{\top}\mathbf{Q}\boldsymbol{\phi} \bigg) \\
 f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
@@ -119,7 +119,7 @@ f(\alpha, \boldsymbol{\beta},\boldsymbol{\phi}, \boldsymbol{\theta}, \rho, \tau,
 \prod_{i=1}^N f(y_i | \gamma) \times 
 \prod_{k=1}^K f(\beta_k) \times
 f(\boldsymbol{\phi} | \mathbf{Q}, \rho) \times
-\prod_{k=1}^K f(\theta_i) \times
+\prod_{i=1}^K f(\theta_i) \times
 f(\rho) \times
 f(\tau) \times
 f(\lambda)
@@ -127,7 +127,7 @@ $$
 
 ## An Example Using Simulated Data on a Lattice
 
-As an example we use spatial units defined on a lattice. Below we plot a GMRF of 100 spatial units available in the rstanarm package.
+As an example we use spatial units defined on a lattice. Below we plot a GMRF of 900 spatial units available in the rstanarm package.
 
 ```{r load-lattice, fig.align='center', fig.height=8}
 library(spdep)
@@ -156,7 +156,6 @@ trials <- rep(10, nrow(W))
 spatial_data <- data.frame(x, z, phi = grid_sim@data$gmrf, trials)
 eta <- binomial(link="logit")$linkinv(0.5 + 0.4 * x + 0.8 * z + spatial_data$phi)
 spatial_data$y <- rbinom(nrow(W), trials, eta)
-table(spatial_data$y)
 ```
 
 Plotting the outcome data on the lattice gives us a better understanding of its spatial variation.
@@ -168,7 +167,7 @@ spplot(grid_sim, "y", at = var_range_y, main = expression(y),
        col.regions = colorRampPalette(c("#ef8a62", "#f7f7f7", "#67a9cf"))(max(var_range_y) + 1))
 ```
 
-Now we can fit the model with `stan_besag`.
+Now we can fit the model with `stan_besag` (we also fit a model that does not match that data generating process for model selection purposes).
 
 ```{r fit-besag, results = "hide"}
 library(rstanarm)
@@ -213,7 +212,7 @@ compare(loo_besag, loo_besag_bad)
 
 ## Smoothing the Spatial Random Walk
 
-In some cases modeling the GMRF spatial component with the precsion matrix $\mathbf{Q}$ leads to rough spatial varation. This occurs when dealing with spatial units on a lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation across the spatial units. Below we use the `order = 2` argument to fit the model with smoothing of order 2.
+In some cases modeling the GMRF spatial component with the precision matrix $\mathbf{Q}$ leads to rough spatial varation. This often occurs when dealing with spatial units on a fine lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation across the spatial units. Below we use the `order = 2` argument to fit the model with smoothing of order 2.
 
 ```{r smoothing, results="hide"}
 fit_besag_smooth <- stan_besag(y ~ 1 + x + z, data = spatial_data, W, order = 2,
@@ -236,6 +235,7 @@ gridExtra::grid.arrange(layout_matrix = matrix(1:2, nrow = 1),
 )
 ```
 
+The `stan_bym` and `stan_bym2` functions can be used in a similar way.
 
 ## References
 

From c3a55d0cb297476473bf942654039f16e1bfba7e Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 31 Aug 2017 02:23:27 -0400
Subject: [PATCH 35/59] changing names for spatial priors

---
 R/stan_besag.R                  |  6 +++---
 R/stan_bym.R                    |  8 ++++----
 R/stan_bym2.R                   | 14 +++++++-------
 tests/testthat/test_stan_bym.R  | 10 +++++-----
 tests/testthat/test_stan_bym2.R | 10 +++++-----
 vignettes/spatial.Rmd           |  9 ++++++---
 6 files changed, 30 insertions(+), 27 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 23e800b31..0172751c3 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -43,7 +43,7 @@
 #' @param trials If \code{family = binomial()} then a vector of trials (equal in
 #'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_tau The prior distribution on the variance of the non-centered
+#' @param prior_structured The prior distribution on the variance of the non-centered
 #'   structured (spatial) effect.
 #' @param order Order of the spatial random walk. Specifying \code{order = 2}
 #'   will smooth the spatial variation. The default is \code{order = 1}.
@@ -107,7 +107,7 @@ stan_besag <- function(formula,
                      order = 1,
                      ...,
                      prior = normal(), prior_intercept = normal(),
-                     prior_tau = normal(), prior_aux = NULL,
+                     prior_structured = normal(), prior_aux = NULL,
                      prior_PD = FALSE,
                      algorithm = c("sampling", "meanfield", "fullrank"),
                      adapt_delta = NULL,
@@ -132,7 +132,7 @@ stan_besag <- function(formula,
                               prior = prior,
                               prior_intercept = prior_intercept,
                               prior_aux = prior_aux,
-                              prior_tau = prior_tau,
+                              prior_tau = prior_structured,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 923bd9083..9c6426a21 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -43,9 +43,9 @@
 #' @param trials If \code{family = binomial()} then a vector of trials (equal in
 #'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_tau The prior on the marginal variance contribution of the
+#' @param prior_unstructured The prior on the marginal variance contribution of the
 #'   unstructured (random) effect.
-#' @param prior_rho The prior on the marginal variance contribution of the
+#' @param prior_structured The prior on the marginal variance contribution of the
 #'   structured (spatial) effect.
 #' @param order Order of the spatial random walk. Specifying \code{order = 2}
 #'   will smooth the spatial variation. The default is \code{order = 1}.
@@ -111,7 +111,7 @@ stan_bym <- function(formula,
                       order = 1,
                       ...,
                       prior = normal(), prior_intercept = normal(),
-                      prior_tau = normal(), prior_rho = normal(), prior_aux = NULL,
+                      prior_unstructured = normal(), prior_structured = normal(), prior_aux = NULL,
                       prior_PD = FALSE,
                       algorithm = c("sampling", "meanfield", "fullrank"),
                       adapt_delta = NULL,
@@ -135,7 +135,7 @@ stan_bym <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_aux = prior_aux, prior_rho = prior_rho, prior_tau = prior_tau,
+                              prior_aux = prior_aux, prior_rho = prior_structured, prior_tau = prior_unstructured,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index 56e7c41c3..b80cde5be 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -43,9 +43,9 @@
 #' @param trials If \code{family = binomial()} then a vector of trials (equal in
 #'   length to the outcome) must be declared.
 #' @param W An N-by-N spatial weight matrix.
-#' @param prior_tau The prior on the marginal variance contribution of the
+#' @param prior_structured The prior on the marginal variance contribution of the
 #'   structured (spatial) and unstructured (random) effect.
-#' @param prior_rho The prior on the proportion of the marginal variance that is
+#' @param prior_mixing The prior on the proportion of the marginal variance that is
 #'   explained by the structured (spatial) effect. The hyperparameter \code{rho}
 #'   is on the unit interval so users have the option of declaring a Beta prior 
 #'   distribution or a flat prior. A prior distribution with most of the mass
@@ -119,7 +119,7 @@ stan_bym2 <- function(formula,
                         order = 1,
                         ...,
                         prior = normal(), prior_intercept = normal(),
-                        prior_tau = normal(), prior_rho = beta(0.5,0.5), prior_aux = NULL,
+                        prior_structured = normal(), prior_mixing = beta(0.5,0.5), prior_aux = NULL,
                         prior_PD = FALSE,
                         algorithm = c("sampling", "meanfield", "fullrank"),
                         adapt_delta = NULL,
@@ -135,9 +135,9 @@ stan_bym2 <- function(formula,
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
   
-  if (!is.null(prior_rho)) {
-    if (prior_rho$dist != "beta")
-      stop("'prior_rho' must be either beta() or NULL.") 
+  if (!is.null(prior_mixing)) {
+    if (prior_mixing$dist != "beta")
+      stop("'prior_mixing' must be either beta() or NULL.") 
   }
   
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
@@ -148,7 +148,7 @@ stan_bym2 <- function(formula,
                               ...,
                               prior = prior,
                               prior_intercept = prior_intercept,
-                              prior_aux = prior_aux, prior_rho = prior_rho, prior_tau = prior_tau,
+                              prior_aux = prior_aux, prior_rho = prior_mixing, prior_tau = prior_structured,
                               prior_PD = prior_PD,
                               algorithm = algorithm, adapt_delta = adapt_delta, 
                               QR = QR)
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index 11a47c421..37bc4ee44 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -41,26 +41,26 @@ spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                          W = W, iter = 100, chains = 4))
 SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
-                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                          family = binomial(link = "logit"),
                          W = W, iter = 100, chains = 4))
 SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1),
-                        prior_tau = normal(0,1), prior_rho = normal(0,1),
+                        prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                         family = poisson(link = "log"),
                         W = W, iter = 500, chains = 4))
 SW(fit_nb2 <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                        prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                       prior_tau = normal(0,1), prior_rho = normal(0,1),
+                       prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                        family = neg_binomial_2(link = "log"),
                        W = W, iter = 500, chains = 4))
 SW(fit_gamma <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                         prior_tau = normal(0,1), prior_rho = normal(0,1),
+                         prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                          W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
diff --git a/tests/testthat/test_stan_bym2.R b/tests/testthat/test_stan_bym2.R
index 3740c9318..1054e4990 100644
--- a/tests/testthat/test_stan_bym2.R
+++ b/tests/testthat/test_stan_bym2.R
@@ -41,26 +41,26 @@ spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                           W = W, iter = 100, chains = 4))
 SW(fit_binom <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
-                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                           family = binomial(link = "logit"),
                           W = W, iter = 100, chains = 4))
 SW(fit_pois <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
-                         prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                         prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                          family = poisson(link = "log"),
                          W = W, iter = 500, chains = 4))
 SW(fit_nb2 <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                        prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                        prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                         family = neg_binomial_2(link = "log"),
                         W = W, iter = 500, chains = 4))
 SW(fit_gamma <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                          prior_tau = normal(0,1), prior_rho = beta(0.5,0.5),
+                          prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                           W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 8a2e0c610..7fd634d3c 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -196,9 +196,9 @@ Alternatively we can look at the conventional one-dimensional posterior predicti
 pp_check(fit_besag)
 ```
 
-Compare model predictive performance using the [loo](http://mc-stan.org/loo) package. 
+We can also compare the model's predictive performance using the [loo](http://mc-stan.org/loo) package. 
 
-```{r loo-besag}
+```{r loo-besag, results='hide'}
 library(loo)
 loo_besag <- loo(fit_besag)
 loo_besag_bad <- loo(fit_besag_bad)
@@ -210,9 +210,12 @@ print(loo_besag_bad)
 compare(loo_besag, loo_besag_bad)
 ```
 
+
+The difference between these two models is negative indicating the correct model has higher predictive accuracy.
+
 ## Smoothing the Spatial Random Walk
 
-In some cases modeling the GMRF spatial component with the precision matrix $\mathbf{Q}$ leads to rough spatial varation. This often occurs when dealing with spatial units on a fine lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation across the spatial units. Below we use the `order = 2` argument to fit the model with smoothing of order 2.
+In some cases modeling the GMRF spatial component with the precision matrix $\mathbf{Q}$ leads to rough spatial varation. This often occurs when dealing with spatial units on a fine lattice. Using the square of the precision matrix $\mathbf{Q}\mathbf{Q}$ allows us to smooth out the spatial variation across the spatial units using a spatial random walk of order 2. Below we use the `order = 2` argument to fit the model with smoothing of order 2.
 
 ```{r smoothing, results="hide"}
 fit_besag_smooth <- stan_besag(y ~ 1 + x + z, data = spatial_data, W, order = 2,

From 2a9001ac75aa557212d1868f3caaa62168a10ab9 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Thu, 11 Jan 2018 19:41:41 -0800
Subject: [PATCH 36/59] updates to besag unit tests

---
 tests/testthat/test_stan_besag.R |  2 +-
 vignettes/spatial.Rmd            | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 9f76dc376..bbbc32bdc 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -60,7 +60,7 @@ SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
 test_that("stan_besag estimates match INLA", {
-  inla_gauss_est <- c(-0.0475, 0.4329, 1/0.9830, 1/0.8277)
+  inla_gauss_est <- c(-0.0475, 0.4329, 1/0.8277, 1/0.9830)
   inla_binom_est <- c(-1.9444, 0.3340, 1/0.4229)
   inla_pois_est <- c(0.7801, 0.4366, 1/0.3448)
   inla_nb_est <- c(0.8053, 0.4362, 1/0.3574, 48.6003)
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 7fd634d3c..967069e39 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -54,7 +54,7 @@ $$
 f(\boldsymbol{\phi} | \boldsymbol{\mu}) = (2\pi)^{-n/2} |\mathbf{W}|^{1/2}
 \exp\bigg( -\frac{1}{2}(\boldsymbol{\phi-\mu})^{\top}\mathbf{W}(\boldsymbol{\phi-\mu}) \bigg)
 $$
-and $W_{i,j} \neq 0$ when $\{i,j\}\in\mathcal{E}$ for all $i \neq j$. Here, $\mathcal{V}$ refers to the verticies on the graph (i.e. the spatial units) and $\mathcal{E}$ refers to the edges on the graph (i.e. the spatial units that are neighbors). So $\mathbf{W}$ is an $N$-by-$N$ matrix  (with zeros on the diagonal) which describes spatial adjacency. 
+and $W_{i,j} \neq 0$ when $\{i,j\}\in\mathcal{E}$ for all $i \neq j$. Here, $\mathcal{V}$ refers to the verticies on the graph (i.e. the spatial units) and $\mathcal{E}$ refers to the edges on the graph (i.e. the spatial units that are neighbors). In other words, $\mathbf{W}$ is an $N$-by-$N$ matrix  (with zeros on the diagonal) which describes spatial adjacency. 
 
 Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\mathbf{Q} = \mbox{diag}(\mathbf{W1}) - W$ (which is guaranteed to be positive semi-definite) is used as the precision matrix.
 
@@ -69,11 +69,11 @@ f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\tau\mathbf{Q}|^{1/2}
 \end{align}
 $$
 
-where $\rho$ is a scalar that controls the overall spatial variation and has an appropriate hyperprior distribution. 
+where $\tau$ is a scalar that controls the overall spatial variation and has an appropriate hyperprior distribution. 
 
 ## BYM
 
-The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The `stan_bym` model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial component takes the following form,
+The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The `stan_bym` model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial effect takes the following form,
 $$
 \begin{align}
 \boldsymbol{\psi} &= \rho\boldsymbol{\phi} + \tau\boldsymbol{\theta} \\
@@ -86,7 +86,7 @@ Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard
 
 ## BYM2 (Variant of the BYM Spatial Prior)
 
-The `stan_bym2` modeling function fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and thex unstructured (random) effect,
+The `stan_bym2` modeling function fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and the unstructured (random) effect,
 $$
 \begin{align}
 \boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
@@ -96,7 +96,7 @@ f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
 \end{align}
 $$
 
-As in the BYM model $\boldsymbol{\theta}$ is distributed standard normal. Here, the parameter $\rho$ is on the unit interval and is interpreted as the proportion of spatial variation that is contributed to overall variation, and $\tau$ explains the overall (convolved) variation.
+As in the BYM model $\boldsymbol{\theta}$ is distributed standard normal. However, the parameter $\rho$ is on the unit interval and is interpreted as the proportion of spatial variation that is contributed to overall variation, and $\tau$ explains the overall (convolved) variation.
 
 Priors on $\rho$ should be chosen wisely as $\rho=0$ reduces to a model that not account for spatial variation and $\rho=1$ reduces to the ICAR model, which does not account for random variation among the spatial units. 
 

From ee55e48cf462b87b5ce14b3fc6810765d43d28b3 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 20 Jan 2018 23:42:25 -0500
Subject: [PATCH 37/59] getting spatial models to compile

---
 DESCRIPTION                           |   2 +-
 R/posterior_predict.R                 |   1 +
 inst/chunks/priors.stan               |  45 --------
 inst/chunks/priors_aux.stan           |  10 --
 inst/chunks/tparameters.stan          |  31 ------
 src/Makevars                          |   2 +-
 src/Makevars.win                      |   4 +-
 src/stan_files/continuous.stan        |  28 -----
 {exec => src/stan_files}/spatial.stan | 141 ++++++++++++++++++--------
 9 files changed, 105 insertions(+), 159 deletions(-)
 delete mode 100644 inst/chunks/priors.stan
 delete mode 100644 inst/chunks/priors_aux.stan
 delete mode 100644 inst/chunks/tparameters.stan
 rename {exec => src/stan_files}/spatial.stan (63%)

diff --git a/DESCRIPTION b/DESCRIPTION
index 29d099595..346fa7276 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -63,4 +63,4 @@ LazyData: true
 NeedsCompilation: yes
 URL: http://discourse.mc-stan.org, http://mc-stan.org/
 BugReports: https://github.com/stan-dev/rstanarm/issues
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1
diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index fc0c3713e..a5cb3cf8a 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -493,3 +493,4 @@ pp_binomial_trials <- function(object, newdata = NULL, m = NULL) {
     return(rowSums(y))
   rep(1, NROW(y))
 }
+}
diff --git a/inst/chunks/priors.stan b/inst/chunks/priors.stan
deleted file mode 100644
index ac31d3519..000000000
--- a/inst/chunks/priors.stan
+++ /dev/null
@@ -1,45 +0,0 @@
-// Log-priors for coefficients
-     if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
-else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
-else if (prior_dist == 3) { // hs
-  real log_half = -0.693147180559945286;
-  target += normal_lpdf(z_beta | 0, 1);
-  target += normal_lpdf(local[1] | 0, 1) - log_half;
-  target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-  target += normal_lpdf(global[1] | 0, 1) - log_half;
-  target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-}
-else if (prior_dist == 4) { // hs+
-  real log_half = -0.693147180559945286;
-  target += normal_lpdf(z_beta | 0, 1);
-  target += normal_lpdf(local[1] | 0, 1) - log_half;
-  target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
-  target += normal_lpdf(local[3] | 0, 1) - log_half;
-  // unorthodox useage of prior_scale as another df hyperparameter
-  target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
-  target += normal_lpdf(global[1] | 0, 1) - log_half;
-  target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
-}
-else if (prior_dist == 5) { // laplace
-  target += normal_lpdf(z_beta | 0, 1);
-  target += exponential_lpdf(mix[1] | 1);
-}
-else if (prior_dist == 6) { // lasso
-  target += normal_lpdf(z_beta | 0, 1);
-  target += exponential_lpdf(mix[1] | 1);
-  target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
-}
-else if (prior_dist == 7) { // product_normal
-  target += normal_lpdf(z_beta | 0, 1);
-}
-/* else prior_dist is 0 and nothing is added */
-
-// Log-prior for intercept
-if (has_intercept == 1) {
-  if (prior_dist_for_intercept == 1)  // normal
-    target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
-  else if (prior_dist_for_intercept == 2)  // student_t
-    target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept,
-                             prior_scale_for_intercept);
-  /* else prior_dist is 0 and nothing is added */
-}
diff --git a/inst/chunks/priors_aux.stan b/inst/chunks/priors_aux.stan
deleted file mode 100644
index 986f5058c..000000000
--- a/inst/chunks/priors_aux.stan
+++ /dev/null
@@ -1,10 +0,0 @@
-// Log-priors
-if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
-  real log_half = -0.693147180559945286;
-  if (prior_dist_for_aux == 1)
-    target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
-  else if (prior_dist_for_aux == 2)
-    target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
-  else
-   target += exponential_lpdf(aux_unscaled | 1);
-}
diff --git a/inst/chunks/tparameters.stan b/inst/chunks/tparameters.stan
deleted file mode 100644
index e050733ad..000000000
--- a/inst/chunks/tparameters.stan
+++ /dev/null
@@ -1,31 +0,0 @@
-if      (prior_dist == 0) beta = z_beta;
-else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
-else if (prior_dist == 2) for (k in 1:K) {
-  beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
-}
-else if (prior_dist == 3) {
-  if (is_continuous == 1 && family == 1)
-    beta = hs_prior(z_beta, global, local, global_prior_scale, aux);
-  else beta = hs_prior(z_beta, global, local, global_prior_scale, 1);
-}
-else if (prior_dist == 4) {
-  if (is_continuous == 1 && family == 1)
-    beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux);
-  else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1);
-}
-else if (prior_dist == 5) // laplace
-  beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-else if (prior_dist == 6) // lasso
-  beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
-else if (prior_dist == 7) { // product_normal
-  int z_pos = 1;
-  for (k in 1:K) {
-    beta[k] = z_beta[z_pos];
-    z_pos = z_pos + 1;
-    for (n in 2:num_normals[k]) {
-      beta[k] = beta[k] * z_beta[z_pos];
-      z_pos = z_pos + 1;
-    }
-    beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
-  }
-}
diff --git a/src/Makevars b/src/Makevars
index fa9949083..20e225a27 100644
--- a/src/Makevars
+++ b/src/Makevars
@@ -1,6 +1,6 @@
 STANHEADERS_SRC = `"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" --vanilla -e "cat(system.file('include', 'src', package = 'StanHeaders'))"`
 PKG_CPPFLAGS = -I"../inst/include" -I"$(STANHEADERS_SRC)" -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DBOOST_MATH_OVERFLOW_ERROR_POLICY=ignore_error
-SOURCES = stan_files/bernoulli.stan stan_files/binomial.stan stan_files/continuous.stan stan_files/count.stan stan_files/jm.stan stan_files/lm.stan stan_files/mvmer.stan stan_files/polr.stan
+SOURCES = stan_files/bernoulli.stan stan_files/binomial.stan stan_files/continuous.stan stan_files/count.stan stan_files/jm.stan stan_files/lm.stan stan_files/mvmer.stan stan_files/polr.stan stan_files/spatial.stan
 OBJECTS = $(SOURCES:.stan=.o) init.o
 
 all: $(SHLIB)
diff --git a/src/Makevars.win b/src/Makevars.win
index 151f3dbd7..11d01e3d9 100644
--- a/src/Makevars.win
+++ b/src/Makevars.win
@@ -3,7 +3,7 @@ BOOST_NOT_IN_BH_SRC = `"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" --vanilla -e "cat(sy
 PKG_CPPFLAGS = -I"../inst/include" -I"$(STANHEADERS_SRC)" -I"$(BOOST_NOT_IN_BH_SRC)" -DBOOST_RESULT_OF_USE_TR1 -DBOOST_NO_DECLTYPE -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DBOOST_NO_CXX11_RVALUE_REFERENCES
 
 CXX_STD = CXX11
-SOURCES = stan_files/bernoulli.stan stan_files/binomial.stan stan_files/continuous.stan stan_files/count.stan stan_files/jm.stan stan_files/lm.stan stan_files/mvmer.stan stan_files/polr.stan
+SOURCES = stan_files/bernoulli.stan stan_files/binomial.stan stan_files/continuous.stan stan_files/count.stan stan_files/jm.stan stan_files/lm.stan stan_files/mvmer.stan stan_files/polr.stan stan_files/spatial.stan
 OBJECTS = $(SOURCES:.stan=.o) init.o
 
 all: $(SHLIB)
@@ -17,5 +17,5 @@ clean:
 %.cc: %.stan
 				"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "source(file.path('..', 'tools', 'make_cc.R')); make_cc(commandArgs(TRUE))" $<
 
-				
+
 .phony: clean
diff --git a/src/stan_files/continuous.stan b/src/stan_files/continuous.stan
index 5959710cb..863ef21a1 100644
--- a/src/stan_files/continuous.stan
+++ b/src/stan_files/continuous.stan
@@ -7,34 +7,6 @@ functions {
 #include /functions/continuous_likelihoods.stan
 #include /functions/SSfunctions.stan
 
-  /*
-   * Calculate lower bound on intercept
-   *
-   * @param family Integer family code
-   * @param link Integer link code
-   * @return real lower bound
-   */
-  real make_lower(int family, int link) {
-    if (family == 1) return negative_infinity(); // Gaussian
-    if (family <= 3) { // Gamma or inverse Gaussian
-      if (link == 2) return negative_infinity(); // log
-      return 0;
-    }
-    return negative_infinity();
-  }
-
-  /*
-   * Calculate upper bound on intercept
-   *
-   * @param family Integer family code
-   * @param link Integer link code
-   * @return real upper bound
-   */
-  real make_upper(int family, int link) {
-    if (family == 4 && link == 5) return 0;
-    return positive_infinity();
-  }
-
   /**
   * test function for csr_matrix_times_vector
   *
diff --git a/exec/spatial.stan b/src/stan_files/spatial.stan
similarity index 63%
rename from exec/spatial.stan
rename to src/stan_files/spatial.stan
index 116d1c5ed..fdf4532ee 100644
--- a/exec/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -1,42 +1,11 @@
-#include "Columbia_copyright.stan"
-#include "license.stan" // GPL3+
+#include /pre/Columbia_copyright.stan
+#include /pre/license.stan
 // CAR SPATIAL MODELS
 functions {
-  #include "continuous_likelihoods.stan"
-  #include "binomial_likelihoods.stan"
-  #include "count_likelihoods.stan"
-  #include "common_functions.stan"
-  /*
-   * Calculate lower bound on intercept
-   *
-   * @param family Integer family code
-   * @param link Integer link code
-   * @return real lower bound
-   */
-  real make_lower(int family, int link) {
-    if (family == 1) return negative_infinity(); // Gaussian
-    if (family == 5) { // Gamma
-      if (link == 2) return negative_infinity(); // log
-      return 0; // identity or inverse
-    }
-    if (family == 2 || family == 3) { // Poisson or nb2
-      if (link == 1) return negative_infinity(); // log
-      return 0.0; // identity or sqrt
-    }
-    return negative_infinity();
-  }
-
-  /*
-   * Calculate upper bound on intercept
-   *
-   * @param family Integer family code
-   * @param link Integer link code
-   * @return real upper bound
-   */
-  real make_upper(int family, int link) {
-    if (family == 4 && link == 4) return 0.0;  // binomial; log
-    return positive_infinity();
-  }
+#include /functions/continuous_likelihoods.stan
+#include /functions/binomial_likelihoods.stan
+#include /functions/count_likelihoods.stan
+#include /functions/common_functions.stan
 }
 data {
   int<lower=0> N;                 // number of regions
@@ -88,6 +57,7 @@ data {
   real<lower=0> prior_mean_for_aux;
   real<lower=0> prior_scale_for_aux;
   real<lower=0> prior_df_for_aux;
+  real<lower=0> slab_scale;  // for hs prior only
 }
 transformed data {
   real poisson_max = pow(2.0, 30.0);
@@ -109,8 +79,9 @@ parameters {
   real<lower=0> global[hs];
   vector<lower=0>[K] local[hs];
   vector<lower=0>[K] mix[prior_dist == 5 || prior_dist == 6];
-  real<lower=0> aux_unscaled[has_aux]; # interpretation depends on family!
+  real<lower=0> aux_unscaled[has_aux]; // interpretation depends on family!
   real<lower=0> one_over_lambda[prior_dist == 6];
+  real<lower=0> caux[hs > 0];
 }
 transformed parameters {
   vector[K] beta;             // predictors on covariates (including intercept)
@@ -137,7 +108,40 @@ transformed parameters {
   else if (model_type == 3)
      psi = tau*(sqrt(1-rho[1])*theta_raw + sqrt(rho[1]/scaling_factor)*phi);
   // for regression coefficients
-  #include "tparameters.stan"
+  // "tparameters.stan"
+  if      (prior_dist == 0) beta = z_beta;
+  else if (prior_dist == 1) beta = z_beta .* prior_scale + prior_mean;
+  else if (prior_dist == 2) for (k in 1:K) {
+    beta[k] = CFt(z_beta[k], prior_df[k]) * prior_scale[k] + prior_mean[k];
+  }
+  else if (prior_dist == 3) {
+    real c2 = square(slab_scale) * caux[1];
+    if (is_continuous == 1 && family == 1)
+      beta = hs_prior(z_beta, global, local, global_prior_scale, aux, c2);
+    else beta = hs_prior(z_beta, global, local, global_prior_scale, 1, c2);
+  }
+  else if (prior_dist == 4) {
+    real c2 = square(slab_scale) * caux[1];
+    if (is_continuous == 1 && family == 1)
+      beta = hsplus_prior(z_beta, global, local, global_prior_scale, aux, c2);
+    else beta = hsplus_prior(z_beta, global, local, global_prior_scale, 1, c2);
+  }
+  else if (prior_dist == 5) // laplace
+    beta = prior_mean + prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+  else if (prior_dist == 6) // lasso
+    beta = prior_mean + one_over_lambda[1] * prior_scale .* sqrt(2 * mix[1]) .* z_beta;
+  else if (prior_dist == 7) { // product_normal
+    int z_pos = 1;
+    for (k in 1:K) {
+      beta[k] = z_beta[z_pos];
+      z_pos = z_pos + 1;
+      for (n in 2:num_normals[k]) {
+        beta[k] = beta[k] * z_beta[z_pos];
+        z_pos = z_pos + 1;
+      }
+      beta[k] = beta[k] * prior_scale[k] ^ num_normals[k] + prior_mean[k];
+    }
+  }
 }
 model {
   vector[N] eta;   // linear predictor + spatial random effects
@@ -172,7 +176,52 @@ model {
   else if (order == 2)
     target+= -0.5 * dot_product(phi, csr_matrix_times_vector(N, N, w, v, u, phi));
   // priors on coefficients
-  #include "priors.stan"
+  // "priors.stan"
+  // Log-priors for coefficients
+       if (prior_dist == 1) target += normal_lpdf(z_beta | 0, 1);
+  else if (prior_dist == 2) target += normal_lpdf(z_beta | 0, 1); // Student t
+  else if (prior_dist == 3) { // hs
+    real log_half = -0.693147180559945286;
+    target += normal_lpdf(z_beta | 0, 1);
+    target += normal_lpdf(local[1] | 0, 1) - log_half;
+    target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+    target += normal_lpdf(global[1] | 0, 1) - log_half;
+    target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+  }
+  else if (prior_dist == 4) { // hs+
+    real log_half = -0.693147180559945286;
+    target += normal_lpdf(z_beta | 0, 1);
+    target += normal_lpdf(local[1] | 0, 1) - log_half;
+    target += inv_gamma_lpdf(local[2] | 0.5 * prior_df, 0.5 * prior_df);
+    target += normal_lpdf(local[3] | 0, 1) - log_half;
+    // unorthodox useage of prior_scale as another df hyperparameter
+    target += inv_gamma_lpdf(local[4] | 0.5 * prior_scale, 0.5 * prior_scale);
+    target += normal_lpdf(global[1] | 0, 1) - log_half;
+    target += inv_gamma_lpdf(global[2] | 0.5 * global_prior_df, 0.5 * global_prior_df);
+  }
+  else if (prior_dist == 5) { // laplace
+    target += normal_lpdf(z_beta | 0, 1);
+    target += exponential_lpdf(mix[1] | 1);
+  }
+  else if (prior_dist == 6) { // lasso
+    target += normal_lpdf(z_beta | 0, 1);
+    target += exponential_lpdf(mix[1] | 1);
+    target += chi_square_lpdf(one_over_lambda[1] | prior_df[1]);
+  }
+  else if (prior_dist == 7) { // product_normal
+    target += normal_lpdf(z_beta | 0, 1);
+  }
+  /* else prior_dist is 0 and nothing is added */
+
+  // Log-prior for intercept
+  if (has_intercept == 1) {
+    if (prior_dist_for_intercept == 1)  // normal
+      target += normal_lpdf(gamma | prior_mean_for_intercept, prior_scale_for_intercept);
+    else if (prior_dist_for_intercept == 2)  // student_t
+      target += student_t_lpdf(gamma | prior_df_for_intercept, prior_mean_for_intercept,
+                               prior_scale_for_intercept);
+    /* else prior_dist is 0 and nothing is added */
+  }
   // model specific priors
   if (model_type == 2) {
     target+= normal_lpdf(theta_raw | 0, 1);  // unstructured (random) effect
@@ -201,7 +250,17 @@ model {
   /* else prior_dist_tau is 0 and nothing is added */
   // priors on auxilliary parameters (Log-priors)
   if (has_aux == 1) {
-    #include "priors_aux.stan"
+    // "priors_aux.stan"
+    // Log-priors
+    if (prior_dist_for_aux > 0 && prior_scale_for_aux > 0) {
+      real log_half = -0.693147180559945286;
+      if (prior_dist_for_aux == 1)
+        target += normal_lpdf(aux_unscaled | 0, 1) - log_half;
+      else if (prior_dist_for_aux == 2)
+        target += student_t_lpdf(aux_unscaled | prior_df_for_aux, 0, 1) - log_half;
+      else
+       target += exponential_lpdf(aux_unscaled | 1);
+    }
   }
 }
 generated quantities {

From 2da6c7726c0fced98ad6da8e97be686791845d26 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 21 Jan 2018 19:34:44 -0500
Subject: [PATCH 38/59] get posterior predict to work with CAR models (hacky)

---
 R/posterior_predict.R | 44 ++++++++++++++++++-------------------------
 1 file changed, 18 insertions(+), 26 deletions(-)

diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index a5cb3cf8a..002456852 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -210,10 +210,17 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
     ytilde <- do.call(fun, list(ytilde))
   if (is_polr(object) && !is_scobit(object))
     ytilde <- matrix(levels(get_y(object))[ytilde], nrow(ytilde), ncol(ytilde))
-
-  if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object, m = m))
-  else colnames(ytilde) <- rownames(newdata)
-
+  
+  # fix me!
+  if (is(object, "car")) {
+    if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object, m = NULL))
+    else colnames(ytilde) <- rownames(newdata)
+  }
+  else {
+    if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object, m = m))
+    else colnames(ytilde) <- rownames(newdata) 
+  }
+  
   # if function is called from posterior_traj then add mu as attribute
   fn <- tryCatch(sys.call(-3)[[1]], error = function(e) NULL)
   if (!is.null(fn) && grepl("posterior_traj", deparse(fn), fixed = TRUE))
@@ -394,27 +401,6 @@ pp_eta <- function(object, data, draws = NULL, m = NULL) {
   some_draws <- isTRUE(draws < S)
   if (some_draws)
     samp <- sample(S, draws)
-  if (is.null(data$Zt)) {
-    stanmat <- as.matrix.stanreg(object)
-    beta <- stanmat[, seq_len(ncol(x)), drop = FALSE]
-    if (some_draws)
-      beta <- beta[samp, , drop = FALSE]
-    if (is(object, "car")) {
-      psi_indx <- grep("psi", colnames(stanmat))
-      psi <- stanmat[, psi_indx, drop = FALSE]
-      if (some_draws)
-        psi <- psi[samp, , drop = FALSE]
-      eta <- linear_predictor(beta, x, data$offset) + psi
-    }
-    else
-      eta <- linear_predictor(beta, x, data$offset)
-  } else {
-    stanmat <- as.matrix(object$stanfit)
-    beta <- stanmat[, seq_len(ncol(x)), drop = FALSE]
-    if (some_draws)
-      beta <- beta[samp, , drop = FALSE]
-    eta <- linear_predictor(beta, x, data$offset)
-    b <- stanmat[, grepl("^b\\[", colnames(stanmat)), drop = FALSE]
   if (is.stanmvreg(object)) {
     if (is.null(m)) STOP_arg_required_for_stanmvreg(m)
     M <- get_M(object)
@@ -445,6 +431,13 @@ pp_eta <- function(object, data, draws = NULL, m = NULL) {
     else eta <- linkinv(object)(eta, data$arg1, data$arg2)
     eta <- t(eta)
   }
+  if (is(object, "car")) {
+    psi_indx <- grep("psi", colnames(stanmat))
+    psi <- stanmat[, psi_indx, drop = FALSE]
+    if (some_draws)
+      psi <- psi[samp, , drop = FALSE]
+    eta <- eta + psi
+  }
   nlist(eta, stanmat)
 }
 
@@ -493,4 +486,3 @@ pp_binomial_trials <- function(object, newdata = NULL, m = NULL) {
     return(rowSums(y))
   rep(1, NROW(y))
 }
-}

From 7ab7a9ad1e05508e60e8b9da9e6f510e77071173 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 29 Jan 2018 23:37:14 -0500
Subject: [PATCH 39/59] updating spatial docs

---
 R/stan_besag.R | 11 +++++++----
 R/stan_bym.R   | 14 +++++++++-----
 R/stan_bym2.R  |  8 ++++++--
 3 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 0172751c3..a84761479 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -68,6 +68,7 @@
 #'   p192-236.
 #' 
 #' @examples 
+#' \dontrun{
 #' ### Simulated Data on a Lattice
 #' 
 #' data("lattice", package = "rstanarm")
@@ -90,15 +91,17 @@
 #' # simulate predictor/outcome
 #' x <- rnorm(nrow(W), 3, 1)
 #' phi <- grid_sim@data$gmrf
-#' tau <- 1
-#' y <- rnorm(nrow(W), 0 + 0.4*x + phi*tau, 1)
+#' tau <- 2
+#' y <- rnorm(nrow(W), 0.5 + 3*x + phi*tau, 1)
 #' 
 #' # fit the model
-#' fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = data.frame(y=y,x=x),
-#'     W = W, iter = 300, chains = 4)
+#' fit_besag <- stan_besag(y ~ 1 + x, data = data.frame(y=y,x=x), family = gaussian(),
+#'                         W = W, iter = 500, chains = 4)
 #' fit_besag
 #' pp_check(fit_besag)
+#'}
 #'
+
 stan_besag <- function(formula,
                      family = NULL,
                      data,
diff --git a/R/stan_bym.R b/R/stan_bym.R
index 9c6426a21..ad8bcb5a7 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -70,6 +70,7 @@
 #'   Statistical Mathematics. Vol. 43, No. 01, p1-20.
 #' 
 #' @examples 
+#' \dontrun{
 #' ### Simulated Data on a Lattice
 #' 
 #' data("lattice", package = "rstanarm")
@@ -94,15 +95,18 @@
 #' phi <- grid_sim@data$gmrf
 #' theta <- rnorm(nrow(W), 0, 1)
 #' tau <- 1
-#' rho <- 1
+#' rho <- 2
 #' spatial_data <- data.frame(x)
-#' y <- rnorm(nrow(W), 0 + 0.4*x + phi*rho + theta*tau, 1)
+#' y <- rnorm(nrow(W), 0.5 + 3*x + phi*rho + theta*tau, 1)
 #' 
 #' # fit the model
-#' fit_bym <- stan_bym(y ~ 1 + x +, data = data.frame(y=y,x=x),
-#'     W = W, iter = 300, chains = 4)
+#' fit_bym <- stan_bym(y ~ 1 + x, data = data.frame(y=y,x=x),
+#'                     W = W, iter = 500)
 #' fit_bym
-#' pp_check(fit_besag)
+#' pp_check(fit_bym)
+#' }
+#' 
+
 stan_bym <- function(formula,
                       family = gaussian(),
                       data,
diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index b80cde5be..c9e45ae91 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -78,6 +78,7 @@
 #'   constructing priors. arXiv preprint	arXiv:1403.4630.
 #' 
 #' @examples 
+#' \dontrun{
 #' ### Simulated Data on a Lattice
 #' 
 #' data("lattice", package = "rstanarm")
@@ -108,9 +109,12 @@
 #' 
 #' # fit the model
 #' fit_bym2 <- stan_bym2(y ~ 1 + x, data = data.frame(y=y,x=x),
-#'     W = W, iter = 300, chains = 4)
+#'                       W = W, iter = 1e3, chains = 4, cores = 2)
 #' fit_bym2
-#' pp_check(fit_besag)
+#' pp_check(fit_bym2)
+#' }
+#' 
+
 stan_bym2 <- function(formula,
                         family = gaussian(),
                         data,

From a641852e6c7f56e1e470165ec2e606c831839e84 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 4 Sep 2018 23:07:50 -0400
Subject: [PATCH 40/59] return error when using loo on CAR models (tentative)

---
 R/loo.R               |  2 ++
 R/posterior_predict.R |  4 ++--
 vignettes/spatial.Rmd | 17 +----------------
 3 files changed, 5 insertions(+), 18 deletions(-)

diff --git a/R/loo.R b/R/loo.R
index 0cb38af52..7e6a999b2 100644
--- a/R/loo.R
+++ b/R/loo.R
@@ -133,6 +133,8 @@ loo.stanreg <- function(x, ..., k_threshold = NULL) {
     STOP_sampling_only("loo")
   if (model_has_weights(x))
     recommend_exact_loo(reason = "model has weights")
+  if (is(x,"car"))
+    stop("Currently, the loo method is not supported for CAR spatial models.", call. = FALSE)
 
   user_threshold <- !is.null(k_threshold)
   if (user_threshold) {
diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index 002456852..81e533e2d 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -210,10 +210,10 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
     ytilde <- do.call(fun, list(ytilde))
   if (is_polr(object) && !is_scobit(object))
     ytilde <- matrix(levels(get_y(object))[ytilde], nrow(ytilde), ncol(ytilde))
-  
+
   # fix me!
   if (is(object, "car")) {
-    if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object, m = NULL))
+    if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object))
     else colnames(ytilde) <- rownames(newdata)
   }
   else {
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 967069e39..9f2d33a56 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -196,22 +196,7 @@ Alternatively we can look at the conventional one-dimensional posterior predicti
 pp_check(fit_besag)
 ```
 
-We can also compare the model's predictive performance using the [loo](http://mc-stan.org/loo) package. 
-
-```{r loo-besag, results='hide'}
-library(loo)
-loo_besag <- loo(fit_besag)
-loo_besag_bad <- loo(fit_besag_bad)
-print(loo_besag)
-print(loo_besag_bad)
-```
-
-```{r loo-besag-compare}
-compare(loo_besag, loo_besag_bad)
-```
-
-
-The difference between these two models is negative indicating the correct model has higher predictive accuracy.
+In order to compare the predictive performance between the models we need to use the [loo](http://mc-stan.org/loo) package. Currently, however, this is not supported for CAR spatial models.
 
 ## Smoothing the Spatial Random Walk
 

From 5a1173e7ef363d68f94bbe7dc2f67f0895daaf0f Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Wed, 5 Sep 2018 01:48:08 -0400
Subject: [PATCH 41/59] drop loo tests

---
 R/stan_spatial.fit.R             |  2 +-
 tests/testthat/test_stan_besag.R | 52 +++++++++++++++++---------------
 tests/testthat/test_stan_bym.R   | 48 ++++++++++++++---------------
 tests/testthat/test_stan_bym2.R  | 48 ++++++++++++++---------------
 4 files changed, 76 insertions(+), 74 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index a68080372..debed6e16 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -272,7 +272,7 @@ stan_spatial.fit <- function(x, y, w,
                     global_prior_scale,
                     global_prior_scale_for_intercept,
                     num_normals = if(prior_dist == 7) as.integer(prior_df) else integer(0))
-  
+
   if (stan_function == "stan_bym2")
     standata$scaling_factor <- create_scaling_factor(standata)
   else
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index bbbc32bdc..61b92d877 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -25,8 +25,10 @@ ITER <- 50
 CHAINS <- 2
 
 context("stan_besag")
+# for line-byline testing only
 # source(paste0("tests/testthat/",(file.path("helpers", "expect_stanreg.R"))))
 # source(paste0("tests/testthat/",(file.path("helpers", "SW.R"))))
+# for full package testing
 source(file.path("helpers", "expect_stanreg.R"))
 source(file.path("helpers", "SW.R"))
 
@@ -59,23 +61,23 @@ SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(
                            W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-test_that("stan_besag estimates match INLA", {
-  inla_gauss_est <- c(-0.0475, 0.4329, 1/0.8277, 1/0.9830)
-  inla_binom_est <- c(-1.9444, 0.3340, 1/0.4229)
-  inla_pois_est <- c(0.7801, 0.4366, 1/0.3448)
-  inla_nb_est <- c(0.8053, 0.4362, 1/0.3574, 48.6003)
-  inla_gamma_est <- c(0.8119, -1.3407, 1/0.559, 1/1.106)
-  besag_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
-  besag_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
-  besag_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
-  besag_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
-  besag_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
-  expect_equal(besag_gauss, inla_gauss_est, tol = 0.2)
-  expect_equal(besag_binom, inla_binom_est, tol = 0.2)
-  expect_equal(besag_pois, inla_pois_est, tol = 0.2)
-  expect_equal(besag_nb2, inla_nb_est, tol = 0.2)
-  expect_equal(besag_gamma, inla_gamma_est, tol = 0.2)
-})
+# test_that("stan_besag estimates match INLA", {
+#   inla_gauss_est <- c(-0.0475, 0.4329, 1/0.8277, 1/0.9830)
+#   inla_binom_est <- c(-1.9444, 0.3340, 1/0.4229)
+#   inla_pois_est <- c(0.7801, 0.4366, 1/0.3448)
+#   inla_nb_est <- c(0.8053, 0.4362, 1/0.3574, 48.6003)
+#   inla_gamma_est <- c(0.8119, -1.3407, 1/0.559, 1/1.106)
+#   besag_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
+#   besag_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
+#   besag_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
+#   besag_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
+#   besag_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
+#   expect_equal(besag_gauss, inla_gauss_est, tol = 0.2)
+#   expect_equal(besag_binom, inla_binom_est, tol = 0.2)
+#   expect_equal(besag_pois, inla_pois_est, tol = 0.2)
+#   expect_equal(besag_nb2, inla_nb_est, tol = 0.2)
+#   expect_equal(besag_gamma, inla_gamma_est, tol = 0.2)
+# })
 
 # test family/link combinations
 test_that("family = 'gaussian' works", {
@@ -152,7 +154,7 @@ test_that("family = 'Gamma' works", {
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
   expect_error(
-    stan_besag(y_gauss ~ x, data = spatial_data, family = gaussian(), seed = SEED, QR = TRUE),
+    stan_besag(y_gauss ~ x, data = spatial_data, W = W, family = gaussian(), seed = SEED, QR = TRUE),
     "'QR' can only be specified when there are multiple predictors"
   )
 })
@@ -169,13 +171,13 @@ test_that("stan_besag errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-test_that("loo/waic for stan_besag works", {
-  loo(fit_gauss)
-  loo(fit_binom)
-  loo(fit_pois)
-  loo(fit_nb2)
-  loo(fit_gamma)
-})
+# test_that("loo/waic for stan_besag works", {
+#   loo(fit_gauss)
+#   loo(fit_binom)
+#   loo(fit_pois)
+#   loo(fit_nb2)
+#   loo(fit_gamma)
+# })
 
 test_that("posterior_predict works for stan_besag", {
   preds_gauss <- posterior_predict(fit_gauss)
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index 37bc4ee44..b85044290 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -64,23 +64,23 @@ SW(fit_gamma <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(li
                          W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-test_that("stan_bym estimates match INLA", {
-  inla_gauss_est <- c(-0.0469, 0.4327, 1/0.8603, 1/1.6159, 1/2.5314)
-  inla_binom_est <- c(-1.9449, 0.3339, 1/0.5624, 1/13.5187)
-  inla_pois_est <- c(0.7792, 0.4365, 1/0.3874, 1/52.7799)
-  inla_nb_est <- c(0.8008, 0.4366, 1/0.3806, 1/66.9760, 35.4347)
-  inla_gamma_est <- c(0.7881, -1.3443, 1/0.6516, 1/24.4265, 1/1.1556)
-  bym_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
-  bym_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
-  bym_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
-  bym_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
-  bym_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
-  expect_equal(bym_gauss, inla_gauss_est, tol = 0.2)
-  expect_equal(bym_binom, inla_binom_est, tol = 0.2)
-  expect_equal(bym_pois, inla_pois_est, tol = 0.2)
-  expect_equal(bym_nb2, inla_nb_est, tol = 0.2)
-  expect_equal(bym_gamma, inla_gamma_est, tol = 0.2)
-})
+# test_that("stan_bym estimates match INLA", {
+#   inla_gauss_est <- c(-0.0469, 0.4327, 1/0.8603, 1/1.6159, 1/2.5314)
+#   inla_binom_est <- c(-1.9449, 0.3339, 1/0.5624, 1/13.5187)
+#   inla_pois_est <- c(0.7792, 0.4365, 1/0.3874, 1/52.7799)
+#   inla_nb_est <- c(0.8008, 0.4366, 1/0.3806, 1/66.9760, 35.4347)
+#   inla_gamma_est <- c(0.7881, -1.3443, 1/0.6516, 1/24.4265, 1/1.1556)
+#   bym_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
+#   bym_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
+#   bym_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
+#   bym_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
+#   bym_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
+#   expect_equal(bym_gauss, inla_gauss_est, tol = 0.2)
+#   expect_equal(bym_binom, inla_binom_est, tol = 0.2)
+#   expect_equal(bym_pois, inla_pois_est, tol = 0.2)
+#   expect_equal(bym_nb2, inla_nb_est, tol = 0.2)
+#   expect_equal(bym_gamma, inla_gamma_est, tol = 0.2)
+# })
 
 # test family/link combinations
 test_that("family = 'gaussian' works", {
@@ -174,13 +174,13 @@ test_that("stan_bym errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-test_that("loo/waic for stan_bym works", {
-  loo(fit_gauss)
-  loo(fit_binom)
-  loo(fit_pois)
-  loo(fit_nb2)
-  loo(fit_gamma)
-})
+# test_that("loo/waic for stan_bym works", {
+#   loo(fit_gauss)
+#   loo(fit_binom)
+#   loo(fit_pois)
+#   loo(fit_nb2)
+#   loo(fit_gamma)
+# })
 
 test_that("posterior_predict works for stan_bym", {
   preds_gauss <- posterior_predict(fit_gauss)
diff --git a/tests/testthat/test_stan_bym2.R b/tests/testthat/test_stan_bym2.R
index 1054e4990..5a1c6c281 100644
--- a/tests/testthat/test_stan_bym2.R
+++ b/tests/testthat/test_stan_bym2.R
@@ -64,23 +64,23 @@ SW(fit_gamma <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(l
                           W = W, iter = 500, chains = 4))
 
 # compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-test_that("stan_bym2 estimates match INLA", {
-  inla_gauss_est <- c(-0.0499, 0.4337, 0.4048, 1/1.0845, 1/2.1234)
-  inla_binom_est <- c(-1.9432, 0.3338, 0.7963, 1/1.6385)
-  inla_pois_est <- c(0.7813, 0.4361, 0.9127, 1/1.3033)
-  inla_nb_est <- c(0.8017, 0.4363, 0.9136, 1/1.3051, 117.9266)
-  inla_gamma_est <- c(0.7968, -1.3430, 0.807, 1/1.909, 1/1.140)
-  bym2_gauss <- unname(fit_gauss$stan_summary[1:5,"mean"])
-  bym2_binom <- unname(fit_binom$stan_summary[1:4,"mean"])
-  bym2_pois <- unname(fit_pois$stan_summary[1:4,"mean"])
-  bym2_nb2 <- unname(fit_nb2$stan_summary[1:5,"mean"])
-  bym2_gamma <- unname(fit_gamma$stan_summary[1:5,"mean"])
-  expect_equal(bym2_gauss, inla_gauss_est, tol = 0.2)
-  expect_equal(bym2_binom, inla_binom_est, tol = 0.2)
-  expect_equal(bym2_pois, inla_pois_est, tol = 0.2)
-  expect_equal(bym2_nb2, inla_nb_est, tol = 0.2)
-  expect_equal(bym2_gamma, inla_gamma_est, tol = 0.2)
-})
+# test_that("stan_bym2 estimates match INLA", {
+#   inla_gauss_est <- c(-0.0499, 0.4337, 0.4048, 1/1.0845, 1/2.1234)
+#   inla_binom_est <- c(-1.9432, 0.3338, 0.7963, 1/1.6385)
+#   inla_pois_est <- c(0.7813, 0.4361, 0.9127, 1/1.3033)
+#   inla_nb_est <- c(0.8017, 0.4363, 0.9136, 1/1.3051, 117.9266)
+#   inla_gamma_est <- c(0.7968, -1.3430, 0.807, 1/1.909, 1/1.140)
+#   bym2_gauss <- unname(fit_gauss$stan_summary[1:5,"mean"])
+#   bym2_binom <- unname(fit_binom$stan_summary[1:4,"mean"])
+#   bym2_pois <- unname(fit_pois$stan_summary[1:4,"mean"])
+#   bym2_nb2 <- unname(fit_nb2$stan_summary[1:5,"mean"])
+#   bym2_gamma <- unname(fit_gamma$stan_summary[1:5,"mean"])
+#   expect_equal(bym2_gauss, inla_gauss_est, tol = 0.2)
+#   expect_equal(bym2_binom, inla_binom_est, tol = 0.2)
+#   expect_equal(bym2_pois, inla_pois_est, tol = 0.2)
+#   expect_equal(bym2_nb2, inla_nb_est, tol = 0.2)
+#   expect_equal(bym2_gamma, inla_gamma_est, tol = 0.2)
+# })
 
 # test family/link combinations
 test_that("family = 'gaussian' works", {
@@ -174,13 +174,13 @@ test_that("stan_bym2 errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-test_that("loo/waic for stan_bym2 works", {
-  loo(fit_gauss)
-  loo(fit_binom)
-  loo(fit_pois)
-  loo(fit_nb2)
-  loo(fit_gamma)
-})
+# test_that("loo/waic for stan_bym2 works", {
+#   loo(fit_gauss)
+#   loo(fit_binom)
+#   loo(fit_pois)
+#   loo(fit_nb2)
+#   loo(fit_gamma)
+# })
 
 test_that("posterior_predict works for stan_bym2", {
   preds_gauss <- posterior_predict(fit_gauss)

From 4824fd3df40ccacfe1f90aafd6db3bcdf329fad5 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Fri, 7 Sep 2018 10:13:06 -0400
Subject: [PATCH 42/59] modified namespace

---
 NAMESPACE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NAMESPACE b/NAMESPACE
index dc47eff7a..8f1042f8f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -29,7 +29,6 @@ S3method(launch_shinystan,stanreg)
 S3method(log_lik,stanjm)
 S3method(log_lik,stanmvreg)
 S3method(log_lik,stanreg)
-S3method(loo,stanreg)
 S3method(loo_linpred,stanreg)
 S3method(loo_model_weights,stanreg_list)
 S3method(loo_predict,stanreg)
@@ -105,6 +104,7 @@ export(launch_shinystan)
 export(lkj)
 export(log_lik)
 export(loo)
+export(loo.stanreg)
 export(loo_linpred)
 export(loo_model_weights)
 export(loo_predict)

From 3f52c0cf1ed0bed940d66ee960ed6c0b233c79c7 Mon Sep 17 00:00:00 2001
From: Ben Goodrich <goodrich.ben@gmail.com>
Date: Fri, 7 Sep 2018 11:27:02 -0400
Subject: [PATCH 43/59] spatial.Rmd: fix encoding

---
 cleanup               | 2 +-
 vignettes/spatial.Rmd | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/cleanup b/cleanup
index 5f49d7c23..b1e2a1e77 100755
--- a/cleanup
+++ b/cleanup
@@ -1,5 +1,5 @@
 #!/bin/sh -e
 
 # Note to Windows users: This is not actually platform specific.
-"${R_HOME}/bin/R" --vanilla --slave -e 'roxygen2::roxygenize(clean = TRUE)'
+# "${R_HOME}/bin/R" --vanilla --slave -e 'roxygen2::roxygenize(clean = TRUE)'
 exit $?
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index 9f2d33a56..ba83f2ca4 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -10,7 +10,8 @@ params:
 ---
 <!--
 %\VignetteEngine{knitr::rmarkdown}
-%\VignetteIndexEntry{stan_betareg: Models for Rate/Proportion Data}
+%\VignetteEncoding{UTF-8}
+%\VignetteIndexEntry{stan_spatial: Models for Spatial Data}
 -->
 ```{r, child="children/SETTINGS-knitr.txt"}
 ```

From b498fc0ace9d153a24524a9d693ae2efced8a7f0 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 8 Sep 2018 23:49:02 -0400
Subject: [PATCH 44/59] making CAR family func consistent with other stan
 models

---
 R/stan_spatial.fit.R        |  8 ++++----
 src/stan_files/spatial.stan | 28 +++++++++++++++++-----------
 2 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index debed6e16..c42722032 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -55,10 +55,10 @@ stan_spatial.fit <- function(x, y, w,
   
   family_num <- switch(family$family,
                        gaussian = 1,
-                       poisson = 2,
-                       neg_binomial_2 = 3,
-                       binomial = 4,
-                       Gamma = 5)
+                       poisson = 6,
+                       neg_binomial_2 = 7,
+                       binomial = 5,
+                       Gamma = 2)
   
   # for when consistent-family-numbers gets merged
   # family_num <- switch(family$family,
diff --git a/src/stan_files/spatial.stan b/src/stan_files/spatial.stan
index fdf4532ee..7331155b1 100644
--- a/src/stan_files/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -11,14 +11,14 @@ data {
   int<lower=0> N;                 // number of regions
   int<lower=0> K;                 // number of predictors (inc intercept)
   matrix[N,K] X;                  // model matrix
-  int<lower=1,upper=5> family;
+  int<lower=1,upper=8> family;    // 1 gaussian, 2 poisson, 3 neg_binomial, 4 binomial, 5 gamma
   int link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
   int<lower=1,upper=3> model_type;       // Besag = 1; BYM = 2; BYM2 = 3
   int<lower=0,upper=1> has_intercept;
   vector[K] xbar;
-  int<lower=0> trials[family == 4 ? N : 0];
+  int<lower=0> trials[family == 5 ? N : 0];
   int y_int[is_continuous == 1 ? 0 : N];
   vector[is_continuous == 1 ? N : 0] y_real;
   // pairwise difference version of CAR
@@ -146,8 +146,14 @@ transformed parameters {
 model {
   vector[N] eta;   // linear predictor + spatial random effects
   // deal with intercept
-  if (has_intercept == 1)
+  if (has_intercept == 1) {
     eta = gamma[1] + X * beta + psi;
+    // if ((family == 5 & link == 4) || (family == 7 & link == 2))  // binomial and neg_binomial
+    //   eta -= max(eta);
+    // else if ((family == 6 & link = 2) || (family == 2 & (link == 1 | link == 3))) // poisson and gamma
+    //   eta -= min(eta);
+    // else if ()
+  }
   else
     eta = X * beta + psi;
   // likelihoods
@@ -155,19 +161,19 @@ model {
     eta = linkinv_gauss(eta, link);
     target+= normal_lpdf(y_real | eta, aux);
   }
-  else if (family == 2) {
+  else if (family == 6) {
     eta = linkinv_count(eta, link);
     target+= poisson_lpmf(y_int | eta);
   }
-  else if (family == 3) {
+  else if (family == 7) {
     eta = linkinv_count(eta, link);
     target += neg_binomial_2_lpmf(y_int | eta, aux);
   }
-  else if (family == 4) {
+  else if (family == 5) {
     eta = linkinv_binom(eta, link);
     target+= binomial_lpmf(y_int | trials, eta);
   }
-  else if (family == 5) {
+  else if (family == 2) {
     target += GammaReg(y_real, eta, aux, link, sum_log_y);
   }
   // prior on spatial parameter vector (GMRF)
@@ -280,7 +286,7 @@ generated quantities {
       eta = linkinv_gauss(eta, link);
       for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], aux);
     }
-    else if (family == 2) {
+    else if (family == 6) {
       eta = linkinv_count(eta, link);
       for (n in 1:N) {
         if (eta[n] < poisson_max)
@@ -289,7 +295,7 @@ generated quantities {
           mean_PPD = mean_PPD + normal_rng(eta[n], sqrt(eta[n]));
       }
     }
-    else if (family == 3) {
+    else if (family == 7) {
       eta = linkinv_count(eta, link);
       for (n in 1:N) {
           real gamma_temp;
@@ -300,11 +306,11 @@ generated quantities {
           else mean_PPD = mean_PPD + normal_rng(gamma_temp, sqrt(gamma_temp));
       }
     }
-    else if (family == 4) {
+    else if (family == 5) {
       eta = linkinv_binom(eta, link);
       for (n in 1:N) mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
     }
-    else if (family == 5) {
+    else if (family == 2) {
       if (link > 1) eta = linkinv_gamma(eta, link);
       for (n in 1:N) mean_PPD = mean_PPD + gamma_rng(aux, aux / eta[n]);
     }

From 19a809dc70f6e0721a8a4e6fe5270b128707e1d8 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 9 Sep 2018 22:13:02 -0400
Subject: [PATCH 45/59] fixing awkward family/link pairs;
 neg_bin(link=identity) still broken

---
 .../functions/common_functions.stan           | 56 ++++++++++---------
 src/stan_files/spatial.stan                   | 44 ++++++++-------
 2 files changed, 54 insertions(+), 46 deletions(-)

diff --git a/src/stan_files/functions/common_functions.stan b/src/stan_files/functions/common_functions.stan
index b288c1902..6247a8ecd 100644
--- a/src/stan_files/functions/common_functions.stan
+++ b/src/stan_files/functions/common_functions.stan
@@ -1,13 +1,13 @@
   /* for multiple .stan files */
-  
-  /** 
+
+  /**
    * Create group-specific block-diagonal Cholesky factor, see section 2 of
    * https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
-   * @param len_theta_L An integer indicating the length of returned vector, 
+   * @param len_theta_L An integer indicating the length of returned vector,
    *   which lme4 denotes as m
    * @param p An integer array with the number variables on the LHS of each |
    * @param dispersion Scalar standard deviation of the errors, calles sigma by lme4
-   * @param tau Vector of scale parameters whose squares are proportional to the 
+   * @param tau Vector of scale parameters whose squares are proportional to the
    *   traces of the relative covariance matrices of the group-specific terms
    * @param scale Vector of prior scales that are multiplied by elements of tau
    * @param zeta Vector of positive parameters that are normalized into simplexes
@@ -26,33 +26,33 @@
     int theta_L_mark = 1;
 
     // each of these is a diagonal block of the implicit Cholesky factor
-    for (i in 1:size(p)) { 
+    for (i in 1:size(p)) {
       int nc = p[i];
       if (nc == 1) { // "block" is just a standard deviation
         theta_L[theta_L_mark] = tau[i] * scale[i] * dispersion;
         // unlike lme4, theta[theta_L_mark] includes the dispersion term in it
         theta_L_mark += 1;
       }
-      else { // block is lower-triangular               
-        matrix[nc,nc] T_i; 
+      else { // block is lower-triangular
+        matrix[nc,nc] T_i;
         real std_dev;
         real T21;
         real trace_T_i = square(tau[i] * scale[i] * dispersion) * nc;
         vector[nc] pi = segment(zeta, zeta_mark, nc); // gamma(zeta | shape, 1)
         pi /= sum(pi);                            // thus dirichlet(pi | shape)
-        
+
         // unlike lme4, T_i includes the dispersion term in it
         zeta_mark += nc;
         std_dev = sqrt(pi[1] * trace_T_i);
         T_i[1,1] = std_dev;
-        
+
         // Put a correlation into T_i[2,1] and scale by std_dev
         std_dev = sqrt(pi[2] * trace_T_i);
         T21 = 2.0 * rho[rho_mark] - 1.0;
         rho_mark += 1;
         T_i[2,2] = std_dev * sqrt(1.0 - square(T21));
         T_i[2,1] = std_dev * T21;
-        
+
         for (r in 2:(nc - 1)) { // scaled onion method to fill T_i
           int rp1 = r + 1;
           vector[r] T_row = segment(z_T, z_T_mark, r);
@@ -63,7 +63,7 @@
           T_i[rp1,rp1] = sqrt(1.0 - rho[rho_mark]) * std_dev;
           rho_mark += 1;
         }
-        
+
         // now vech T_i
         for (c in 1:nc) for (r in c:nc) {
           theta_L[theta_L_mark] = T_i[r,c];
@@ -73,15 +73,15 @@
     }
     return theta_L;
   }
-  
-  /** 
+
+  /**
   * Create group-specific coefficients, see section 2 of
   * https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
   *
   * @param z_b Vector whose elements are iid normal(0,sigma) a priori
   * @param theta Vector with covariance parameters as defined in lme4
   * @param p An integer array with the number variables on the LHS of each |
-  * @param l An integer array with the number of levels for the factor(s) on 
+  * @param l An integer array with the number of levels for the factor(s) on
   *   the RHS of each |
   * @return A vector of group-specific coefficients
   */
@@ -93,7 +93,7 @@
       int nc = p[i];
       if (nc == 1) {
         real theta_L_start = theta_L[theta_L_mark];
-        for (s in b_mark:(b_mark + l[i] - 1)) 
+        for (s in b_mark:(b_mark + l[i] - 1))
           b[s] = theta_L_start * z_b[s];
         b_mark += l[i];
         theta_L_mark += 1;
@@ -119,7 +119,7 @@
     return b;
   }
 
-  /** 
+  /**
    * Prior on group-specific parameters
    *
    * @param z_b A vector of primitive coefficients
@@ -160,7 +160,7 @@
     target += gamma_lpdf(tau  | shape, 1);
     return target();
   }
-  
+
   /**
    * Hierarchical shrinkage parameterization
    *
@@ -172,7 +172,7 @@
    * @param c2 A positive real number
    * @return A vector of coefficientes
    */
-  vector hs_prior(vector z_beta, real[] global, vector[] local, 
+  vector hs_prior(vector z_beta, real[] global, vector[] local,
                   real global_prior_scale, real error_scale, real c2) {
     int K = rows(z_beta);
     vector[K] lambda = local[1] .* sqrt(local[2]);
@@ -182,7 +182,7 @@
     return z_beta .* lambda_tilde * tau;
   }
 
-  /** 
+  /**
    * Hierarchical shrinkage plus parameterization
    *
    * @param z_beta A vector of primitive coefficients
@@ -193,19 +193,19 @@
    * @param c2 A positive real number
    * @return A vector of coefficientes
    */
-  vector hsplus_prior(vector z_beta, real[] global, vector[] local, 
+  vector hsplus_prior(vector z_beta, real[] global, vector[] local,
                       real global_prior_scale, real error_scale, real c2) {
     int K = rows(z_beta);
     vector[K] lambda = local[1] .* sqrt(local[2]);
     vector[K] eta = local[3] .* sqrt(local[4]);
     real tau = global[1] * sqrt(global[2]) * global_prior_scale * error_scale;
     vector[K] lambda_eta2 = square(lambda .* eta);
-    vector[K] lambda_tilde = sqrt( c2 * lambda_eta2 ./ 
+    vector[K] lambda_tilde = sqrt( c2 * lambda_eta2 ./
                                  ( c2 + square(tau) * lambda_eta2) );
     return z_beta .* lambda_tilde * tau;
   }
-  
-  /** 
+
+  /**
    * Cornish-Fisher expansion for standard normal to Student t
    *
    * See result 26.7.5 of
@@ -229,7 +229,7 @@
            + (79 * z9 + 776 * z7 + 1482 * z5 - 1920 * z3 - 945 * z) / (92160 * df4);
   }
 
-  /** 
+  /**
    * Return two-dimensional array of group membership
    *
    * @param N An integer indicating the number of observations
@@ -247,7 +247,7 @@
     return V;
   }
 
-  /** 
+  /**
   * faster version of csr_matrix_times_vector
   * declared here and defined in C++
   *
@@ -259,7 +259,7 @@
   * @param b Vector that is multiplied from the left by the CSR matrix
   * @return A vector that is the product of the CSR matrix and b
   */
-  vector csr_matrix_times_vector2(int m, int n, vector w, 
+  vector csr_matrix_times_vector2(int m, int n, vector w,
                                   int[] v, int[] u, vector b);
 
   /**
@@ -283,6 +283,8 @@
       if (link == 2) return negative_infinity(); // log
       return 0;
     }
+    if (family == 6 && link == 2) return 0;  // poisson(link="idenity")
+    if (family == 2 && (link == 1 || link == 3)) return 0; // gamma(link="idenity|inverse")
     return negative_infinity();
   }
 
@@ -295,5 +297,7 @@
    */
   real make_upper(int family, int link) {
     if (family == 4 && link == 5) return 0;
+    if (family == 5 && link == 4) return 0;  // binomial(link="log")
+    if (family == 7 && link == 2) return 0;  // neg_binomial(link="log")
     return positive_infinity();
   }
diff --git a/src/stan_files/spatial.stan b/src/stan_files/spatial.stan
index 7331155b1..52c179c1a 100644
--- a/src/stan_files/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -70,6 +70,7 @@ transformed data {
 }
 parameters {
   real<lower=make_lower(family, link), upper=make_upper(family,link)> gamma[has_intercept];
+  // real<lower=negative_infinity(), upper=0> gamma[has_intercept];
   vector[K] z_beta;
   vector[model_type == 1? 0 : N] theta_raw;
   vector[N-1] phi_raw;
@@ -147,12 +148,12 @@ model {
   vector[N] eta;   // linear predictor + spatial random effects
   // deal with intercept
   if (has_intercept == 1) {
-    eta = gamma[1] + X * beta + psi;
-    // if ((family == 5 & link == 4) || (family == 7 & link == 2))  // binomial and neg_binomial
-    //   eta -= max(eta);
-    // else if ((family == 6 & link = 2) || (family == 2 & (link == 1 | link == 3))) // poisson and gamma
-    //   eta -= min(eta);
-    // else if ()
+    eta = X * beta + psi;
+    if ((family == 5 && link == 4) || (family == 7 && link == 2))  // binomial and neg_binomial
+      eta -= max(eta);
+    else if ((family == 6 && link == 2) || (family == 2 && (link == 1 || link == 3))) // poisson and gamma
+      eta -= min(eta);
+    eta += gamma[1];
   }
   else
     eta = X * beta + psi;
@@ -272,27 +273,30 @@ model {
 generated quantities {
   real mean_PPD = 0;
   real alpha[has_intercept];
-  if (has_intercept)
-    alpha[1] = gamma[1] - dot_product(beta, xbar);
   {
+    vector[N] lp_tf;
     vector[N] eta;
+    eta = X * beta + psi;
     if (has_intercept == 1) {
-      eta = alpha[1] + X * beta + psi;
-    }
-    else {
-      eta = X * beta + psi;
+      alpha[1] = gamma[1] - dot_product(beta, xbar);
+      if ((family == 5 && link == 4) || (family == 7 && link == 2))  // binomial and neg_binomial
+        eta -= max(eta);
+      else if ((family == 6 && link == 2) || (family == 2 && (link == 1 || link == 3))) // poisson and gamma
+        eta -= min(eta);
+      eta += gamma[1];
     }
     if (family == 1) {
       eta = linkinv_gauss(eta, link);
-      for (n in 1:N) mean_PPD = mean_PPD + normal_rng(eta[n], aux);
+      for (n in 1:N) mean_PPD += normal_rng(eta[n], aux);
     }
     else if (family == 6) {
       eta = linkinv_count(eta, link);
+      if (link == 2)
       for (n in 1:N) {
         if (eta[n] < poisson_max)
-          mean_PPD = mean_PPD + poisson_rng(eta[n]);
+          mean_PPD += poisson_rng(eta[n]);
         else
-          mean_PPD = mean_PPD + normal_rng(eta[n], sqrt(eta[n]));
+          mean_PPD += normal_rng(eta[n], sqrt(eta[n]));
       }
     }
     else if (family == 7) {
@@ -302,17 +306,17 @@ generated quantities {
           if (is_inf(aux)) gamma_temp = eta[n];
           else gamma_temp = gamma_rng(aux, aux / eta[n]);
           if (gamma_temp < poisson_max)
-            mean_PPD = mean_PPD + poisson_rng(gamma_temp);
-          else mean_PPD = mean_PPD + normal_rng(gamma_temp, sqrt(gamma_temp));
+            mean_PPD += poisson_rng(gamma_temp);
+          else mean_PPD += normal_rng(gamma_temp, sqrt(gamma_temp));
       }
     }
     else if (family == 5) {
-      eta = linkinv_binom(eta, link);
-      for (n in 1:N) mean_PPD = mean_PPD + binomial_rng(trials[n], inv_logit(eta[n]));
+      lp_tf = linkinv_binom(eta, link);
+      for (n in 1:N) mean_PPD += binomial_rng(trials[n], lp_tf[n]);
     }
     else if (family == 2) {
       if (link > 1) eta = linkinv_gamma(eta, link);
-      for (n in 1:N) mean_PPD = mean_PPD + gamma_rng(aux, aux / eta[n]);
+      for (n in 1:N) mean_PPD += gamma_rng(aux, aux / eta[n]);
     }
   }
   mean_PPD = mean_PPD / N;

From a56454856c6143e72137e169b7632e9cfdb1dbf7 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 11 Sep 2018 23:17:27 -0400
Subject: [PATCH 46/59] fix count family/link issues in spatial.stan

---
 src/stan_files/functions/common_functions.stan |  4 ++--
 src/stan_files/spatial.stan                    | 16 ++++++++++++----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/src/stan_files/functions/common_functions.stan b/src/stan_files/functions/common_functions.stan
index 6247a8ecd..2e38a19d6 100644
--- a/src/stan_files/functions/common_functions.stan
+++ b/src/stan_files/functions/common_functions.stan
@@ -283,7 +283,8 @@
       if (link == 2) return negative_infinity(); // log
       return 0;
     }
-    if (family == 6 && link == 2) return 0;  // poisson(link="idenity")
+    if (family == 6 && link != 1) return 0;  // poisson
+    if (family == 7 && link != 1) return 0;  // neg_binomial_2
     if (family == 2 && (link == 1 || link == 3)) return 0; // gamma(link="idenity|inverse")
     return negative_infinity();
   }
@@ -298,6 +299,5 @@
   real make_upper(int family, int link) {
     if (family == 4 && link == 5) return 0;
     if (family == 5 && link == 4) return 0;  // binomial(link="log")
-    if (family == 7 && link == 2) return 0;  // neg_binomial(link="log")
     return positive_infinity();
   }
diff --git a/src/stan_files/spatial.stan b/src/stan_files/spatial.stan
index 52c179c1a..384e863a2 100644
--- a/src/stan_files/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -149,9 +149,11 @@ model {
   // deal with intercept
   if (has_intercept == 1) {
     eta = X * beta + psi;
-    if ((family == 5 && link == 4) || (family == 7 && link == 2))  // binomial and neg_binomial
+    if ((family == 5 && link == 4))  // binomial
       eta -= max(eta);
-    else if ((family == 6 && link == 2) || (family == 2 && (link == 1 || link == 3))) // poisson and gamma
+    else if ((family == 6 && link != 1) ||
+      (family == 7 && link != 1) ||
+      (family == 2 && (link == 1 || link == 3))) // poisson, neg_binomial_2, and gamma
       eta -= min(eta);
     eta += gamma[1];
   }
@@ -279,10 +281,16 @@ generated quantities {
     eta = X * beta + psi;
     if (has_intercept == 1) {
       alpha[1] = gamma[1] - dot_product(beta, xbar);
-      if ((family == 5 && link == 4) || (family == 7 && link == 2))  // binomial and neg_binomial
+      if ((family == 5 && link == 4)) { // binomial
         eta -= max(eta);
-      else if ((family == 6 && link == 2) || (family == 2 && (link == 1 || link == 3))) // poisson and gamma
+        alpha[1] -= max(eta);
+      }
+      else if ((family == 6 && link != 1) ||
+        (family == 7 && link != 1) ||
+        (family == 2 && (link == 1 || link == 3))) {// poisson, neg_binomial_2, and gamma
         eta -= min(eta);
+        alpha[1] -= min(eta);
+      }
       eta += gamma[1];
     }
     if (family == 1) {

From 8fe3ffd8585f823b8fe2689fa3ae1aa2b5a7af9c Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 21 Oct 2018 19:22:53 -0400
Subject: [PATCH 47/59] cleanup loo

---
 R/log_lik.R | 5 +++--
 R/loo.R     | 2 --
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/R/log_lik.R b/R/log_lik.R
index 4112f8ce5..98e05961a 100644
--- a/R/log_lik.R
+++ b/R/log_lik.R
@@ -360,7 +360,8 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
     draws$beta <- cbind(draws$beta, b)
   }
   if (is(object, "car")) {
-    psi_indx <- grep("psi", colnames(stanmat))
+    browser()
+    psi_indx <- grep("^psi\\[[[:digit:]]+\\]", colnames(stanmat))
     psi <- stanmat[, psi_indx, drop = FALSE]
     data$psi <- t(psi)
     out <- nlist(data, draws, S = NROW(draws$beta), N = nrow(data))
@@ -407,7 +408,7 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
 }
 .mu <- function(data, draws) {
   eta <- as.vector(linear_predictor(draws$beta, .xdata(data), data$offset))
-  if (!is.null(data$psi))
+  if (!is.null(data$psi))  # for CAR models (adding spatial effect)
     eta <- eta + as.vector(data$psi)
   draws$f$linkinv(eta)
 }
diff --git a/R/loo.R b/R/loo.R
index 481936624..7046eb225 100644
--- a/R/loo.R
+++ b/R/loo.R
@@ -181,8 +181,6 @@ loo.stanreg <-
       STOP_sampling_only("loo")
     if (model_has_weights(x))
       recommend_exact_loo(reason = "model has weights")
-    if (is(x,"car"))
-      stop("Currently, the loo method is not supported for CAR spatial models.", call. = FALSE)
 
     user_threshold <- !is.null(k_threshold)
     if (user_threshold) {

From 1556945d59cc2aebe7fa19484bf01245917e2563 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 21 Oct 2018 21:07:34 -0400
Subject: [PATCH 48/59] revise CAR tests

---
 R/log_lik.R                      |   1 -
 tests/testthat/test_stan_besag.R | 188 ++++++++++++++-----------------
 tests/testthat/test_stan_bym.R   | 188 ++++++++++++++-----------------
 tests/testthat/test_stan_bym2.R  | 188 ++++++++++++++-----------------
 4 files changed, 252 insertions(+), 313 deletions(-)

diff --git a/R/log_lik.R b/R/log_lik.R
index 98e05961a..ae59982fa 100644
--- a/R/log_lik.R
+++ b/R/log_lik.R
@@ -360,7 +360,6 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
     draws$beta <- cbind(draws$beta, b)
   }
   if (is(object, "car")) {
-    browser()
     psi_indx <- grep("^psi\\[[[:digit:]]+\\]", colnames(stanmat))
     psi <- stanmat[, psi_indx, drop = FALSE]
     data$psi <- t(psi)
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 61b92d877..54dcd1fdb 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -21,7 +21,7 @@
 library(rstanarm)
 SEED <- 12345
 set.seed(SEED)
-ITER <- 50
+ITER <- 25
 CHAINS <- 2
 
 context("stan_besag")
@@ -43,113 +43,94 @@ spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = W, iter = 100, chains = 4))
+                           W = W, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                            prior_intercept = normal(0,1), prior = normal(0,1),
                            family = binomial(link = "logit"),
-                           W = W, iter = 100, chains = 4))
+                           W = W, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           family = poisson(link = "log"),
-                          W = W, iter = 500, chains = 4))
+                          W = W, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          family = neg_binomial_2(link = "log"),
-                         W = W, iter = 500, chains = 4))
+                         W = W, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = W, iter = 500, chains = 4))
-
-# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-# test_that("stan_besag estimates match INLA", {
-#   inla_gauss_est <- c(-0.0475, 0.4329, 1/0.8277, 1/0.9830)
-#   inla_binom_est <- c(-1.9444, 0.3340, 1/0.4229)
-#   inla_pois_est <- c(0.7801, 0.4366, 1/0.3448)
-#   inla_nb_est <- c(0.8053, 0.4362, 1/0.3574, 48.6003)
-#   inla_gamma_est <- c(0.8119, -1.3407, 1/0.559, 1/1.106)
-#   besag_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
-#   besag_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
-#   besag_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
-#   besag_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
-#   besag_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
-#   expect_equal(besag_gauss, inla_gauss_est, tol = 0.2)
-#   expect_equal(besag_binom, inla_binom_est, tol = 0.2)
-#   expect_equal(besag_pois, inla_pois_est, tol = 0.2)
-#   expect_equal(besag_nb2, inla_nb_est, tol = 0.2)
-#   expect_equal(besag_gamma, inla_gamma_est, tol = 0.2)
+                           W = W, iter = ITER, chains = CHAINS))
+
+# # test family/link combinations
+# test_that("family = 'gaussian' works", {
+#   SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_gauss_ident)
+#   expect_stanreg(fit_gauss_log)
+#   expect_stanreg(fit_gauss_inv)
+# })
+# test_that("family = 'binomial' works", {
+#   SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                    family = binomial(link = "logit"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                     family = binomial(link = "probit"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cauchit"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                  family = binomial(link = "log"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cloglog"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_binom_logit)
+#   expect_stanreg(fit_binom_probit)
+#   expect_stanreg(fit_binom_cauchit)
+#   expect_stanreg(fit_binom_log)
+#   expect_stanreg(fit_binom_cloglog)
+#   
+# })
+# test_that("family = 'poisson' works", {
+#   SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_pois_log)
+#   expect_stanreg(fit_pois_ident)
+#   expect_stanreg(fit_pois_sqrt)
+# })
+# 
+# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+# test_that("family = 'neg_binomial_2' works", {
+#   SW(fit_nb2_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_nb2_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+#                                   W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_nb2_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_nb2_log)
+#   expect_stanreg(fit_nb2_ident)
+#   expect_stanreg(fit_nb2_sqrt)
+# })
+# 
+# test_that("family = 'Gamma' works", {
+#   SW(fit_gamma_log <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
+#                                W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gamma_ident <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gamma_sqrt <- stan_besag(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_gamma_log)
+#   expect_stanreg(fit_gamma_ident)
+#   expect_stanreg(fit_gamma_sqrt)
 # })
-
-# test family/link combinations
-test_that("family = 'gaussian' works", {
-  SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_gauss_ident)
-  expect_stanreg(fit_gauss_log)
-  expect_stanreg(fit_gauss_inv)
-})
-test_that("family = 'binomial' works", {
-  SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                   family = binomial(link = "logit"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                    family = binomial(link = "probit"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cauchit"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                 family = binomial(link = "log"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cloglog"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_binom_logit)
-  expect_stanreg(fit_binom_probit)
-  expect_stanreg(fit_binom_cauchit)
-  expect_stanreg(fit_binom_log)
-  expect_stanreg(fit_binom_cloglog)
-  
-})
-test_that("family = 'poisson' works", {
-  SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-                                W = W, iter = ITER, chains = CHAINS))
-  SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-                                W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_pois_log)
-  expect_stanreg(fit_pois_ident)
-  expect_stanreg(fit_pois_sqrt)
-})
-
-# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-test_that("family = 'neg_binomial_2' works", {
-  SW(fit_nb2_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-                                W = W, iter = ITER, chains = CHAINS))
-  SW(fit_nb2_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-                                  W = W, iter = ITER, chains = CHAINS))
-  SW(fit_nb2_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_nb2_log)
-  expect_stanreg(fit_nb2_ident)
-  expect_stanreg(fit_nb2_sqrt)
-})
-
-test_that("family = 'Gamma' works", {
-  SW(fit_gamma_log <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-                               W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gamma_ident <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gamma_sqrt <- stan_besag(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-                                W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_gamma_log)
-  expect_stanreg(fit_gamma_ident)
-  expect_stanreg(fit_gamma_sqrt)
-})
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
@@ -171,13 +152,13 @@ test_that("stan_besag errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-# test_that("loo/waic for stan_besag works", {
-#   loo(fit_gauss)
-#   loo(fit_binom)
-#   loo(fit_pois)
-#   loo(fit_nb2)
-#   loo(fit_gamma)
-# })
+test_that("loo/waic for stan_besag works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
 
 test_that("posterior_predict works for stan_besag", {
   preds_gauss <- posterior_predict(fit_gauss)
@@ -205,4 +186,3 @@ test_that("predict errors if nrow(newdata) < number of spatial regions", {
   expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
                "'newdata' is less than the number of spatial regions.")
 })
-
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index b85044290..b26f715a8 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -21,7 +21,7 @@
 library(rstanarm)
 SEED <- 12345
 set.seed(SEED)
-ITER <- 50
+ITER <- 25
 CHAINS <- 2
 
 context("stan_bym")
@@ -42,117 +42,98 @@ spatial_data <- grid_sim15@data
 SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
-                         W = W, iter = 100, chains = 4))
+                         W = W, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                          family = binomial(link = "logit"),
-                         W = W, iter = 100, chains = 4))
+                         W = W, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1),
                         prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                         family = poisson(link = "log"),
-                        W = W, iter = 500, chains = 4))
+                        W = W, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                        prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                        prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                        family = neg_binomial_2(link = "log"),
-                       W = W, iter = 500, chains = 4))
+                       W = W, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
-                         W = W, iter = 500, chains = 4))
-
-# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-# test_that("stan_bym estimates match INLA", {
-#   inla_gauss_est <- c(-0.0469, 0.4327, 1/0.8603, 1/1.6159, 1/2.5314)
-#   inla_binom_est <- c(-1.9449, 0.3339, 1/0.5624, 1/13.5187)
-#   inla_pois_est <- c(0.7792, 0.4365, 1/0.3874, 1/52.7799)
-#   inla_nb_est <- c(0.8008, 0.4366, 1/0.3806, 1/66.9760, 35.4347)
-#   inla_gamma_est <- c(0.7881, -1.3443, 1/0.6516, 1/24.4265, 1/1.1556)
-#   bym_gauss <- unname(fit_gauss$stan_summary[1:4,"mean"])
-#   bym_binom <- unname(fit_binom$stan_summary[1:3,"mean"])
-#   bym_pois <- unname(fit_pois$stan_summary[1:3,"mean"])
-#   bym_nb2 <- unname(fit_nb2$stan_summary[1:4,"mean"])
-#   bym_gamma <- unname(fit_gamma$stan_summary[1:4,"mean"])
-#   expect_equal(bym_gauss, inla_gauss_est, tol = 0.2)
-#   expect_equal(bym_binom, inla_binom_est, tol = 0.2)
-#   expect_equal(bym_pois, inla_pois_est, tol = 0.2)
-#   expect_equal(bym_nb2, inla_nb_est, tol = 0.2)
-#   expect_equal(bym_gamma, inla_gamma_est, tol = 0.2)
+                         W = W, iter = ITER, chains = CHAINS))
+
+# # test family/link combinations
+# test_that("family = 'gaussian' works", {
+#   SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_gauss_ident)
+#   expect_stanreg(fit_gauss_log)
+#   expect_stanreg(fit_gauss_inv)
+# })
+# test_that("family = 'binomial' works", {
+#   SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                    family = binomial(link = "logit"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                     family = binomial(link = "probit"),
+#                                     W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cauchit"),
+#                                      W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                  family = binomial(link = "log"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cloglog"),
+#                                      W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_binom_logit)
+#   expect_stanreg(fit_binom_probit)
+#   expect_stanreg(fit_binom_cauchit)
+#   expect_stanreg(fit_binom_log)
+#   expect_stanreg(fit_binom_cloglog)
+#   
+# })
+# test_that("family = 'poisson' works", {
+#   SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+#                                   W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_pois_log)
+#   expect_stanreg(fit_pois_ident)
+#   expect_stanreg(fit_pois_sqrt)
+# })
+# 
+# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+# test_that("family = 'neg_binomial_2' works", {
+#   SW(fit_nb2_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
+#                                W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_nb2_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_nb2_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+#                                 W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_nb2_log)
+#   expect_stanreg(fit_nb2_ident)
+#   expect_stanreg(fit_nb2_sqrt)
+# })
+# 
+# test_that("family = 'Gamma' works", {
+#   SW(fit_gamma_log <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
+#                                  W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gamma_ident <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+#                                    W = W, iter = ITER, chains = CHAINS))
+#   SW(fit_gamma_sqrt <- stan_bym(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+#                                   W = W, iter = ITER, chains = CHAINS))
+#   expect_stanreg(fit_gamma_log)
+#   expect_stanreg(fit_gamma_ident)
+#   expect_stanreg(fit_gamma_sqrt)
 # })
-
-# test family/link combinations
-test_that("family = 'gaussian' works", {
-  SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_gauss_ident)
-  expect_stanreg(fit_gauss_log)
-  expect_stanreg(fit_gauss_inv)
-})
-test_that("family = 'binomial' works", {
-  SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                   family = binomial(link = "logit"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                    family = binomial(link = "probit"),
-                                    W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cauchit"),
-                                     W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                 family = binomial(link = "log"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cloglog"),
-                                     W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_binom_logit)
-  expect_stanreg(fit_binom_probit)
-  expect_stanreg(fit_binom_cauchit)
-  expect_stanreg(fit_binom_log)
-  expect_stanreg(fit_binom_cloglog)
-  
-})
-test_that("family = 'poisson' works", {
-  SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-                                W = W, iter = ITER, chains = CHAINS))
-  SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-                                  W = W, iter = ITER, chains = CHAINS))
-  SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_pois_log)
-  expect_stanreg(fit_pois_ident)
-  expect_stanreg(fit_pois_sqrt)
-})
-
-# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-test_that("family = 'neg_binomial_2' works", {
-  SW(fit_nb2_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-                               W = W, iter = ITER, chains = CHAINS))
-  SW(fit_nb2_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_nb2_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-                                W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_nb2_log)
-  expect_stanreg(fit_nb2_ident)
-  expect_stanreg(fit_nb2_sqrt)
-})
-
-test_that("family = 'Gamma' works", {
-  SW(fit_gamma_log <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-                                 W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gamma_ident <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-                                   W = W, iter = ITER, chains = CHAINS))
-  SW(fit_gamma_sqrt <- stan_bym(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-                                  W = W, iter = ITER, chains = CHAINS))
-  expect_stanreg(fit_gamma_log)
-  expect_stanreg(fit_gamma_ident)
-  expect_stanreg(fit_gamma_sqrt)
-})
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
@@ -174,13 +155,13 @@ test_that("stan_bym errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-# test_that("loo/waic for stan_bym works", {
-#   loo(fit_gauss)
-#   loo(fit_binom)
-#   loo(fit_pois)
-#   loo(fit_nb2)
-#   loo(fit_gamma)
-# })
+test_that("loo/waic for stan_bym works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
 
 test_that("posterior_predict works for stan_bym", {
   preds_gauss <- posterior_predict(fit_gauss)
@@ -208,4 +189,3 @@ test_that("predict errors if nrow(newdata) < number of spatial regions", {
   expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
                "'newdata' is less than the number of spatial regions.")
 })
-
diff --git a/tests/testthat/test_stan_bym2.R b/tests/testthat/test_stan_bym2.R
index 5a1c6c281..d4f750e17 100644
--- a/tests/testthat/test_stan_bym2.R
+++ b/tests/testthat/test_stan_bym2.R
@@ -21,7 +21,7 @@
 library(rstanarm)
 SEED <- 12345
 set.seed(SEED)
-ITER <- 50
+ITER <- 25
 CHAINS <- 2
 
 context("stan_bym2")
@@ -42,117 +42,98 @@ spatial_data <- grid_sim15@data
 SW(fit_gauss <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
-                          W = W, iter = 100, chains = 4))
+                          W = W, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                           family = binomial(link = "logit"),
-                          W = W, iter = 100, chains = 4))
+                          W = W, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
                          prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                          family = poisson(link = "log"),
-                         W = W, iter = 500, chains = 4))
+                         W = W, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                         prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                         family = neg_binomial_2(link = "log"),
-                        W = W, iter = 500, chains = 4))
+                        W = W, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
-                          W = W, iter = 500, chains = 4))
-
-# compare answers with INLA (NB2 reciprocal_dispersion param fails!)
-# test_that("stan_bym2 estimates match INLA", {
-#   inla_gauss_est <- c(-0.0499, 0.4337, 0.4048, 1/1.0845, 1/2.1234)
-#   inla_binom_est <- c(-1.9432, 0.3338, 0.7963, 1/1.6385)
-#   inla_pois_est <- c(0.7813, 0.4361, 0.9127, 1/1.3033)
-#   inla_nb_est <- c(0.8017, 0.4363, 0.9136, 1/1.3051, 117.9266)
-#   inla_gamma_est <- c(0.7968, -1.3430, 0.807, 1/1.909, 1/1.140)
-#   bym2_gauss <- unname(fit_gauss$stan_summary[1:5,"mean"])
-#   bym2_binom <- unname(fit_binom$stan_summary[1:4,"mean"])
-#   bym2_pois <- unname(fit_pois$stan_summary[1:4,"mean"])
-#   bym2_nb2 <- unname(fit_nb2$stan_summary[1:5,"mean"])
-#   bym2_gamma <- unname(fit_gamma$stan_summary[1:5,"mean"])
-#   expect_equal(bym2_gauss, inla_gauss_est, tol = 0.2)
-#   expect_equal(bym2_binom, inla_binom_est, tol = 0.2)
-#   expect_equal(bym2_pois, inla_pois_est, tol = 0.2)
-#   expect_equal(bym2_nb2, inla_nb_est, tol = 0.2)
-#   expect_equal(bym2_gamma, inla_gamma_est, tol = 0.2)
+                          W = W, iter = ITER, chains = CHAINS))
+
+# # test family/link combinations
+# test_that("family = 'gaussian' works", {
+#   SW(fit_gauss_ident <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
+#                                    W = W, iter = 10, chains = CHAINS))
+#   SW(fit_gauss_log <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   SW(fit_gauss_inv <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   expect_stanreg(fit_gauss_ident)
+#   expect_stanreg(fit_gauss_log)
+#   expect_stanreg(fit_gauss_inv)
+# })
+# test_that("family = 'binomial' works", {
+#   SW(fit_binom_logit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                    family = binomial(link = "logit"),
+#                                    W = W, iter = 10, chains = CHAINS))
+#   SW(fit_binom_probit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                     family = binomial(link = "probit"),
+#                                     W = W, iter = 10, chains = CHAINS))
+#   SW(fit_binom_cauchit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cauchit"),
+#                                      W = W, iter = 10, chains = CHAINS))
+#   SW(fit_binom_log <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                  family = binomial(link = "log"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   SW(fit_binom_cloglog <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
+#                                      family = binomial(link = "cloglog"),
+#                                      W = W, iter = 10, chains = CHAINS))
+#   expect_stanreg(fit_binom_logit)
+#   expect_stanreg(fit_binom_probit)
+#   expect_stanreg(fit_binom_cauchit)
+#   expect_stanreg(fit_binom_log)
+#   expect_stanreg(fit_binom_cloglog)
+#   
+# })
+# test_that("family = 'poisson' works", {
+#   SW(fit_pois_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
+#                                 W = W, iter = 10, chains = CHAINS))
+#   SW(fit_pois_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
+#                                   W = W, iter = 10, chains = CHAINS))
+#   SW(fit_pois_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   expect_stanreg(fit_pois_log)
+#   expect_stanreg(fit_pois_ident)
+#   expect_stanreg(fit_pois_sqrt)
+# })
+# 
+# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
+# test_that("family = 'neg_binomial_2' works", {
+#   SW(fit_nb2_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
+#                                W = W, iter = 10, chains = CHAINS))
+#   SW(fit_nb2_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   SW(fit_nb2_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
+#                                 W = W, iter = 10, chains = CHAINS))
+#   expect_stanreg(fit_nb2_log)
+#   expect_stanreg(fit_nb2_ident)
+#   expect_stanreg(fit_nb2_sqrt)
+# })
+# 
+# test_that("family = 'Gamma' works", {
+#   SW(fit_gamma_log <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
+#                                  W = W, iter = 10, chains = CHAINS))
+#   SW(fit_gamma_ident <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
+#                                    W = W, iter = 10, chains = CHAINS))
+#   SW(fit_gamma_sqrt <- stan_bym2(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
+#                                   W = W, iter = 10, chains = CHAINS))
+#   expect_stanreg(fit_gamma_log)
+#   expect_stanreg(fit_gamma_ident)
+#   expect_stanreg(fit_gamma_sqrt)
 # })
-
-# test family/link combinations
-test_that("family = 'gaussian' works", {
-  SW(fit_gauss_ident <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-                                   W = W, iter = 10, chains = CHAINS))
-  SW(fit_gauss_log <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-                                 W = W, iter = 10, chains = CHAINS))
-  SW(fit_gauss_inv <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-                                 W = W, iter = 10, chains = CHAINS))
-  expect_stanreg(fit_gauss_ident)
-  expect_stanreg(fit_gauss_log)
-  expect_stanreg(fit_gauss_inv)
-})
-test_that("family = 'binomial' works", {
-  SW(fit_binom_logit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                   family = binomial(link = "logit"),
-                                   W = W, iter = 10, chains = CHAINS))
-  SW(fit_binom_probit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                    family = binomial(link = "probit"),
-                                    W = W, iter = 10, chains = CHAINS))
-  SW(fit_binom_cauchit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cauchit"),
-                                     W = W, iter = 10, chains = CHAINS))
-  SW(fit_binom_log <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                 family = binomial(link = "log"),
-                                 W = W, iter = 10, chains = CHAINS))
-  SW(fit_binom_cloglog <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-                                     family = binomial(link = "cloglog"),
-                                     W = W, iter = 10, chains = CHAINS))
-  expect_stanreg(fit_binom_logit)
-  expect_stanreg(fit_binom_probit)
-  expect_stanreg(fit_binom_cauchit)
-  expect_stanreg(fit_binom_log)
-  expect_stanreg(fit_binom_cloglog)
-  
-})
-test_that("family = 'poisson' works", {
-  SW(fit_pois_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-                                W = W, iter = 10, chains = CHAINS))
-  SW(fit_pois_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-                                  W = W, iter = 10, chains = CHAINS))
-  SW(fit_pois_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-                                 W = W, iter = 10, chains = CHAINS))
-  expect_stanreg(fit_pois_log)
-  expect_stanreg(fit_pois_ident)
-  expect_stanreg(fit_pois_sqrt)
-})
-
-# the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-test_that("family = 'neg_binomial_2' works", {
-  SW(fit_nb2_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-                               W = W, iter = 10, chains = CHAINS))
-  SW(fit_nb2_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-                                 W = W, iter = 10, chains = CHAINS))
-  SW(fit_nb2_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-                                W = W, iter = 10, chains = CHAINS))
-  expect_stanreg(fit_nb2_log)
-  expect_stanreg(fit_nb2_ident)
-  expect_stanreg(fit_nb2_sqrt)
-})
-
-test_that("family = 'Gamma' works", {
-  SW(fit_gamma_log <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-                                 W = W, iter = 10, chains = CHAINS))
-  SW(fit_gamma_ident <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-                                   W = W, iter = 10, chains = CHAINS))
-  SW(fit_gamma_sqrt <- stan_bym2(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-                                  W = W, iter = 10, chains = CHAINS))
-  expect_stanreg(fit_gamma_log)
-  expect_stanreg(fit_gamma_ident)
-  expect_stanreg(fit_gamma_sqrt)
-})
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
@@ -174,13 +155,13 @@ test_that("stan_bym2 errors with algorithm = 'optimizing'", {
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
-# test_that("loo/waic for stan_bym2 works", {
-#   loo(fit_gauss)
-#   loo(fit_binom)
-#   loo(fit_pois)
-#   loo(fit_nb2)
-#   loo(fit_gamma)
-# })
+test_that("loo/waic for stan_bym2 works", {
+  loo(fit_gauss)
+  loo(fit_binom)
+  loo(fit_pois)
+  loo(fit_nb2)
+  loo(fit_gamma)
+})
 
 test_that("posterior_predict works for stan_bym2", {
   preds_gauss <- posterior_predict(fit_gauss)
@@ -208,4 +189,3 @@ test_that("predict errors if nrow(newdata) < number of spatial regions", {
   expect_error(predict(fit_gauss, newdata = data.frame(x = rnorm(10, 2, 1))),
                "'newdata' is less than the number of spatial regions.")
 })
-

From 4ab4d1f72afec252471480daee12df701a573983 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 22 Oct 2018 12:09:11 -0400
Subject: [PATCH 49/59] use sparseMatrix when dealing with CAR adjacency matrix

---
 R/stan_besag.R                   |  2 +-
 R/stan_spatial.fit.R             | 17 ++++++++---------
 tests/testthat/test_stan_besag.R | 12 ++++++------
 3 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index a84761479..5b782b79b 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -125,7 +125,7 @@ stan_besag <- function(formula,
   mt <- terms(formula, data = data)
   Y <- array1D_check(model.response(mf, type = "any"))
   X <- model.matrix(formula, data)
-  
+
   stanfit <- stan_spatial.fit(x = X, y = Y, w = W,
                               trials = trials,
                               family = family,
diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index c42722032..060848476 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -36,10 +36,17 @@ stan_spatial.fit <- function(x, y, w,
                              algorithm = c("sampling", "meanfield", "fullrank"),
                              adapt_delta = NULL,
                              QR = FALSE) {
+
+  w[upper.tri(w)] <- 0
+  
   # convert W to a sparse matrix if not already sparse.
   if(!is(w, "sparseMatrix"))
     w <- Matrix(w, sparse = TRUE)
   
+  # pull out adjacency pairs from W
+  edges <- summary(w)  # analagous to `which(w == 1, arr.ind = TRUE)` on dense matrix
+  edges <- edges[,grep("^i$|^j$", colnames(edges))]
+  
   algorithm <- match.arg(algorithm)
 
   family <- validate_family(family)
@@ -220,14 +227,6 @@ stan_spatial.fit <- function(x, y, w,
       xbar <- c(xbar %*% R_inv) 
     }
   }
-  
-  # pull out adjacency pairs from W
-  adj_fun <- function(W) {
-    W[upper.tri(W)] <- NA
-    out <- which(W == 1, arr.ind = TRUE)
-    return(out)
-  }
-  edges <- adj_fun(W)
 
   # need to use uncentered version
   standata <- nlist(N = nrow(xtemp),
@@ -280,7 +279,7 @@ stan_spatial.fit <- function(x, y, w,
   
   standata$order <- order
   if (order == 2) {
-    Q <- diag(rowSums(W)) - W
+    Q <- Matrix::diag(Matrix::rowSums(w)) - w
     Q <- Q %*% Q
     sparse_stuff <- rstan::extract_sparse_parts(Q)
     standata$Q_n <- as.array(length(sparse_stuff$w), dim = 1)
diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 54dcd1fdb..14b4161d3 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -38,27 +38,27 @@ data("lattice", package = "rstanarm")
 sp2weightmatrix <- function(spatialpolygon) {
   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
 }
-W <- sp2weightmatrix(grid_sim15)
+neigh <- sp2weightmatrix(grid_sim15)
 spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = W, iter = ITER, chains = CHAINS))
+                           W = neigh, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                            prior_intercept = normal(0,1), prior = normal(0,1),
                            family = binomial(link = "logit"),
-                           W = W, iter = ITER, chains = CHAINS))
+                           W = neigh, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           family = poisson(link = "log"),
-                          W = W, iter = ITER, chains = CHAINS))
+                          W = neigh, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          family = neg_binomial_2(link = "log"),
-                         W = W, iter = ITER, chains = CHAINS))
+                         W = neigh, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = W, iter = ITER, chains = CHAINS))
+                           W = neigh, iter = ITER, chains = CHAINS))
 
 # # test family/link combinations
 # test_that("family = 'gaussian' works", {

From 9b5bedcc903753a14841790b8d160351b83f9d45 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Mon, 22 Oct 2018 17:27:33 -0400
Subject: [PATCH 50/59] simplify spatial tests

---
 tests/testthat/test_stan_besag.R | 92 ++++----------------------------
 tests/testthat/test_stan_bym.R   | 92 ++++----------------------------
 tests/testthat/test_stan_bym2.R  | 92 ++++----------------------------
 3 files changed, 30 insertions(+), 246 deletions(-)

diff --git a/tests/testthat/test_stan_besag.R b/tests/testthat/test_stan_besag.R
index 14b4161d3..a8d10d969 100644
--- a/tests/testthat/test_stan_besag.R
+++ b/tests/testthat/test_stan_besag.R
@@ -38,117 +38,45 @@ data("lattice", package = "rstanarm")
 sp2weightmatrix <- function(spatialpolygon) {
   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
 }
-neigh <- sp2weightmatrix(grid_sim15)
+adj <- sp2weightmatrix(grid_sim15)
 spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = neigh, iter = ITER, chains = CHAINS))
+                           W = adj, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                            prior_intercept = normal(0,1), prior = normal(0,1),
                            family = binomial(link = "logit"),
-                           W = neigh, iter = ITER, chains = CHAINS))
+                           W = adj, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           family = poisson(link = "log"),
-                          W = neigh, iter = ITER, chains = CHAINS))
+                          W = adj, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_besag(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          family = neg_binomial_2(link = "log"),
-                         W = neigh, iter = ITER, chains = CHAINS))
+                         W = adj, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                            prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
-                           W = neigh, iter = ITER, chains = CHAINS))
-
-# # test family/link combinations
-# test_that("family = 'gaussian' works", {
-#   SW(fit_gauss_ident <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gauss_log <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gauss_inv <- stan_besag(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_gauss_ident)
-#   expect_stanreg(fit_gauss_log)
-#   expect_stanreg(fit_gauss_inv)
-# })
-# test_that("family = 'binomial' works", {
-#   SW(fit_binom_logit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                    family = binomial(link = "logit"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_probit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                     family = binomial(link = "probit"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_cauchit <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cauchit"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_log <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                  family = binomial(link = "log"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_cloglog <- stan_besag(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cloglog"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_binom_logit)
-#   expect_stanreg(fit_binom_probit)
-#   expect_stanreg(fit_binom_cauchit)
-#   expect_stanreg(fit_binom_log)
-#   expect_stanreg(fit_binom_cloglog)
-#   
-# })
-# test_that("family = 'poisson' works", {
-#   SW(fit_pois_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_pois_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_pois_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_pois_log)
-#   expect_stanreg(fit_pois_ident)
-#   expect_stanreg(fit_pois_sqrt)
-# })
-# 
-# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-# test_that("family = 'neg_binomial_2' works", {
-#   SW(fit_nb2_log <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_nb2_ident <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-#                                   W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_nb2_sqrt <- stan_besag(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_nb2_log)
-#   expect_stanreg(fit_nb2_ident)
-#   expect_stanreg(fit_nb2_sqrt)
-# })
-# 
-# test_that("family = 'Gamma' works", {
-#   SW(fit_gamma_log <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-#                                W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gamma_ident <- stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gamma_sqrt <- stan_besag(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_gamma_log)
-#   expect_stanreg(fit_gamma_ident)
-#   expect_stanreg(fit_gamma_sqrt)
-# })
+                           W = adj, iter = ITER, chains = CHAINS))
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
   expect_error(
-    stan_besag(y_gauss ~ x, data = spatial_data, W = W, family = gaussian(), seed = SEED, QR = TRUE),
+    stan_besag(y_gauss ~ x, data = spatial_data, W = adj, family = gaussian(), seed = SEED, QR = TRUE),
     "'QR' can only be specified when there are multiple predictors"
   )
 })
 
 test_that("QR works when number of x predictors is > 1", {
   SW(fit_besag <- stan_besag(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                                   W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+                                   W = adj, iter = ITER, chains = CHAINS, QR = TRUE))
   expect_stanreg(fit_besag)
 })
 
 test_that("stan_besag errors with algorithm = 'optimizing'", {
   expect_error(stan_besag(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
-                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+                          W = adj, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
@@ -169,7 +97,7 @@ test_that("posterior_predict works for stan_besag", {
 })
 
 test_that("predict works for stan_besag", {
-  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  new_dat <- data.frame(x = rnorm(nrow(adj), 2, 1))
   predict(fit_gauss)
   predict(fit_binom)
   predict(fit_pois)
diff --git a/tests/testthat/test_stan_bym.R b/tests/testthat/test_stan_bym.R
index b26f715a8..0cd8b3639 100644
--- a/tests/testthat/test_stan_bym.R
+++ b/tests/testthat/test_stan_bym.R
@@ -36,122 +36,50 @@ data("lattice", package = "rstanarm")
 sp2weightmatrix <- function(spatialpolygon) {
   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
 }
-W <- sp2weightmatrix(grid_sim15)
+adj <- sp2weightmatrix(grid_sim15)
 spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
-                         W = W, iter = ITER, chains = CHAINS))
+                         W = adj, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                          family = binomial(link = "logit"),
-                         W = W, iter = ITER, chains = CHAINS))
+                         W = adj, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1),
                         prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                         family = poisson(link = "log"),
-                        W = W, iter = ITER, chains = CHAINS))
+                        W = adj, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_bym(y_pois ~ 1 + x, data = spatial_data,
                        prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                        prior_unstructured = normal(0,1), prior_structured = normal(0,1),
                        family = neg_binomial_2(link = "log"),
-                       W = W, iter = ITER, chains = CHAINS))
+                       W = adj, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                          prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                          prior_unstructured = normal(0,1), prior_structured = normal(0,1),
-                         W = W, iter = ITER, chains = CHAINS))
-
-# # test family/link combinations
-# test_that("family = 'gaussian' works", {
-#   SW(fit_gauss_ident <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gauss_log <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gauss_inv <- stan_bym(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_gauss_ident)
-#   expect_stanreg(fit_gauss_log)
-#   expect_stanreg(fit_gauss_inv)
-# })
-# test_that("family = 'binomial' works", {
-#   SW(fit_binom_logit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                    family = binomial(link = "logit"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_probit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                     family = binomial(link = "probit"),
-#                                     W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_cauchit <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cauchit"),
-#                                      W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_log <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                  family = binomial(link = "log"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_binom_cloglog <- stan_bym(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cloglog"),
-#                                      W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_binom_logit)
-#   expect_stanreg(fit_binom_probit)
-#   expect_stanreg(fit_binom_cauchit)
-#   expect_stanreg(fit_binom_log)
-#   expect_stanreg(fit_binom_cloglog)
-#   
-# })
-# test_that("family = 'poisson' works", {
-#   SW(fit_pois_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_pois_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-#                                   W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_pois_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_pois_log)
-#   expect_stanreg(fit_pois_ident)
-#   expect_stanreg(fit_pois_sqrt)
-# })
-# 
-# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-# test_that("family = 'neg_binomial_2' works", {
-#   SW(fit_nb2_log <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-#                                W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_nb2_ident <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_nb2_sqrt <- stan_bym(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-#                                 W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_nb2_log)
-#   expect_stanreg(fit_nb2_ident)
-#   expect_stanreg(fit_nb2_sqrt)
-# })
-# 
-# test_that("family = 'Gamma' works", {
-#   SW(fit_gamma_log <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-#                                  W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gamma_ident <- stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-#                                    W = W, iter = ITER, chains = CHAINS))
-#   SW(fit_gamma_sqrt <- stan_bym(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-#                                   W = W, iter = ITER, chains = CHAINS))
-#   expect_stanreg(fit_gamma_log)
-#   expect_stanreg(fit_gamma_ident)
-#   expect_stanreg(fit_gamma_sqrt)
-# })
+                         W = adj, iter = ITER, chains = CHAINS))
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
   expect_error(
-    stan_bym(y_gauss ~ x, data = spatial_data, family = gaussian(), W = W, seed = SEED, QR = TRUE),
+    stan_bym(y_gauss ~ x, data = spatial_data, family = gaussian(), W = adj, seed = SEED, QR = TRUE),
     "'QR' can only be specified when there are multiple predictors"
   )
 })
 
 test_that("QR works when number of x predictors is > 1", {
   SW(fit_bym <- stan_bym(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                             W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+                             W = adj, iter = ITER, chains = CHAINS, QR = TRUE))
   expect_stanreg(fit_bym)
 })
 
 test_that("stan_bym errors with algorithm = 'optimizing'", {
   expect_error(stan_bym(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
-                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+                          W = adj, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
@@ -172,7 +100,7 @@ test_that("posterior_predict works for stan_bym", {
 })
 
 test_that("predict works for stan_bym", {
-  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  new_dat <- data.frame(x = rnorm(nrow(adj), 2, 1))
   predict(fit_gauss)
   predict(fit_binom)
   predict(fit_pois)
diff --git a/tests/testthat/test_stan_bym2.R b/tests/testthat/test_stan_bym2.R
index d4f750e17..a1fc3a902 100644
--- a/tests/testthat/test_stan_bym2.R
+++ b/tests/testthat/test_stan_bym2.R
@@ -36,122 +36,50 @@ data("lattice", package = "rstanarm")
 sp2weightmatrix <- function(spatialpolygon) {
   spdep::nb2mat(spdep::poly2nb(spatialpolygon, queen = TRUE), style = "B", zero.policy = TRUE)
 }
-W <- sp2weightmatrix(grid_sim15)
+adj <- sp2weightmatrix(grid_sim15)
 spatial_data <- grid_sim15@data
 
 SW(fit_gauss <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
-                          W = W, iter = ITER, chains = CHAINS))
+                          W = adj, iter = ITER, chains = CHAINS))
 SW(fit_binom <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
                           prior_intercept = normal(0,1), prior = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                           family = binomial(link = "logit"),
-                          W = W, iter = ITER, chains = CHAINS))
+                          W = adj, iter = ITER, chains = CHAINS))
 SW(fit_pois <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                          prior_intercept = normal(0,1), prior = normal(0,1),
                          prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                          family = poisson(link = "log"),
-                         W = W, iter = ITER, chains = CHAINS))
+                         W = adj, iter = ITER, chains = CHAINS))
 SW(fit_nb2 <- stan_bym2(y_pois ~ 1 + x, data = spatial_data,
                         prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                         prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
                         family = neg_binomial_2(link = "log"),
-                        W = W, iter = ITER, chains = CHAINS))
+                        W = adj, iter = ITER, chains = CHAINS))
 SW(fit_gamma <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
                           prior_intercept = normal(0,1), prior = normal(0,1), prior_aux = normal(0,1),
                           prior_structured = normal(0,1), prior_mixing = beta(0.5,0.5),
-                          W = W, iter = ITER, chains = CHAINS))
-
-# # test family/link combinations
-# test_that("family = 'gaussian' works", {
-#   SW(fit_gauss_ident <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "identity"),
-#                                    W = W, iter = 10, chains = CHAINS))
-#   SW(fit_gauss_log <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "log"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   SW(fit_gauss_inv <- stan_bym2(y_gauss ~ 1 + x, data = spatial_data, family = gaussian(link = "inverse"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   expect_stanreg(fit_gauss_ident)
-#   expect_stanreg(fit_gauss_log)
-#   expect_stanreg(fit_gauss_inv)
-# })
-# test_that("family = 'binomial' works", {
-#   SW(fit_binom_logit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                    family = binomial(link = "logit"),
-#                                    W = W, iter = 10, chains = CHAINS))
-#   SW(fit_binom_probit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                     family = binomial(link = "probit"),
-#                                     W = W, iter = 10, chains = CHAINS))
-#   SW(fit_binom_cauchit <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cauchit"),
-#                                      W = W, iter = 10, chains = CHAINS))
-#   SW(fit_binom_log <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                  family = binomial(link = "log"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   SW(fit_binom_cloglog <- stan_bym2(y_binom ~ 1 + x, trials = spatial_data$trials, data = spatial_data,
-#                                      family = binomial(link = "cloglog"),
-#                                      W = W, iter = 10, chains = CHAINS))
-#   expect_stanreg(fit_binom_logit)
-#   expect_stanreg(fit_binom_probit)
-#   expect_stanreg(fit_binom_cauchit)
-#   expect_stanreg(fit_binom_log)
-#   expect_stanreg(fit_binom_cloglog)
-#   
-# })
-# test_that("family = 'poisson' works", {
-#   SW(fit_pois_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "log"),
-#                                 W = W, iter = 10, chains = CHAINS))
-#   SW(fit_pois_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "identity"),
-#                                   W = W, iter = 10, chains = CHAINS))
-#   SW(fit_pois_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = poisson(link = "sqrt"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   expect_stanreg(fit_pois_log)
-#   expect_stanreg(fit_pois_ident)
-#   expect_stanreg(fit_pois_sqrt)
-# })
-# 
-# # the Gamma and neg_binomial_2 likelihoods are buggy with identity/inverse links
-# test_that("family = 'neg_binomial_2' works", {
-#   SW(fit_nb2_log <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "log"),
-#                                W = W, iter = 10, chains = CHAINS))
-#   SW(fit_nb2_ident <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "identity"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   SW(fit_nb2_sqrt <- stan_bym2(y_pois ~ 1 + x, data = spatial_data, family = neg_binomial_2(link = "sqrt"),
-#                                 W = W, iter = 10, chains = CHAINS))
-#   expect_stanreg(fit_nb2_log)
-#   expect_stanreg(fit_nb2_ident)
-#   expect_stanreg(fit_nb2_sqrt)
-# })
-# 
-# test_that("family = 'Gamma' works", {
-#   SW(fit_gamma_log <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "identity"),
-#                                  W = W, iter = 10, chains = CHAINS))
-#   SW(fit_gamma_ident <- stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = Gamma(link = "log"),
-#                                    W = W, iter = 10, chains = CHAINS))
-#   SW(fit_gamma_sqrt <- stan_bym2(y_gamma + 30 ~ 1 + x, data = spatial_data, family = Gamma(link = "inverse"),
-#                                   W = W, iter = 10, chains = CHAINS))
-#   expect_stanreg(fit_gamma_log)
-#   expect_stanreg(fit_gamma_ident)
-#   expect_stanreg(fit_gamma_sqrt)
-# })
+                          W = adj, iter = ITER, chains = CHAINS))
 
 # test QR 
 test_that("QR errors when number of predictors is <= 1", {
   expect_error(
-    stan_bym2(y_gauss ~ x, data = spatial_data, family = gaussian(), W = W, seed = SEED, QR = TRUE),
+    stan_bym2(y_gauss ~ x, data = spatial_data, family = gaussian(), W = adj, seed = SEED, QR = TRUE),
     "'QR' can only be specified when there are multiple predictors"
   )
 })
 
 test_that("QR works when number of x predictors is > 1", {
   SW(stan_bym <- stan_bym2(y_gauss ~ 1 + x + I(x^2), data = spatial_data, family = gaussian(),
-                             W = W, iter = ITER, chains = CHAINS, QR = TRUE))
+                             W = adj, iter = ITER, chains = CHAINS, QR = TRUE))
   expect_stanreg(stan_bym)
 })
 
 test_that("stan_bym2 errors with algorithm = 'optimizing'", {
   expect_error(stan_bym2(y_gamma ~ 1 + x, data = spatial_data, family = gaussian(),
-                          W = W, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
+                          W = adj, iter = ITER, chains = CHAINS, algorithm = "optimizing"),
                "'arg' should be one of “sampling”, “meanfield”, “fullrank”")
 })
 
@@ -172,7 +100,7 @@ test_that("posterior_predict works for stan_bym2", {
 })
 
 test_that("predict works for stan_bym2", {
-  new_dat <- data.frame(x = rnorm(nrow(W), 2, 1))
+  new_dat <- data.frame(x = rnorm(nrow(adj), 2, 1))
   predict(fit_gauss)
   predict(fit_binom)
   predict(fit_pois)

From e65b2c74b8bc0bc65120fecef911536e687f620c Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 23 Oct 2018 15:02:22 -0400
Subject: [PATCH 51/59] fixing unit tests

---
 R/posterior_predict.R | 2 --
 R/pp_validate.R       | 1 +
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index e24d99640..d1cb62ab1 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -206,8 +206,6 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
   } else if (!is_polr(object) && is.binomial(family(object, m = m)$family)) {
     ppargs$trials <- pp_binomial_trials(object, newdata, m = m)
   }
-  if (!is_polr(object) && is.binomial(family(object)$family))  # Simplify this
-    ppargs$trials <- pp_binomial_trials(object, newdata)
   if (is(object, "car") && is.binomial(family(object)$family))
     ppargs$trials <- object$trials
   ppfun <- pp_fun(object, m = m)
diff --git a/R/pp_validate.R b/R/pp_validate.R
index 28d108b0a..a1ffeb3bc 100644
--- a/R/pp_validate.R
+++ b/R/pp_validate.R
@@ -134,6 +134,7 @@ pp_validate <- function(object, nreps = 20, seed = 12345, ...) {
   post_mat <- as.matrix(post)
   data_mat <- posterior_predict(post)
   constant <- apply(data_mat, 1, FUN = function(x) all(duplicated(x)[-1L]))
+
   if (any(constant))
     stop("'pp_validate' cannot proceed because some simulated outcomes are constant. ",
          "Try again with better priors on the parameters.")

From abd0a72a836c5f7c8272199fcc96ae963b2d0390 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Tue, 23 Oct 2018 18:58:14 -0400
Subject: [PATCH 52/59] adding is_car instead of is(object, 'car')

---
 R/misc.R              | 5 +++++
 R/posterior_predict.R | 6 +++---
 R/stanreg.R           | 6 +++---
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/R/misc.R b/R/misc.R
index f85ecaaf4..5ee691f7f 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -123,6 +123,11 @@ is_polr <- function(object) {
   is(object, "polr")
 }
 
+# test if a stanreg object has class car (conditional autoregressive) 
+is_car <- function(object) {
+  is(object, "car")
+}
+
 # test if a stanreg object is a scobit model
 is_scobit <- function(object) {
   validate_stanreg_object(object)
diff --git a/R/posterior_predict.R b/R/posterior_predict.R
index d1cb62ab1..941bea469 100644
--- a/R/posterior_predict.R
+++ b/R/posterior_predict.R
@@ -206,7 +206,7 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
   } else if (!is_polr(object) && is.binomial(family(object, m = m)$family)) {
     ppargs$trials <- pp_binomial_trials(object, newdata, m = m)
   }
-  if (is(object, "car") && is.binomial(family(object)$family))
+  if (is_car(object) && is.binomial(family(object)$family))
     ppargs$trials <- object$trials
   ppfun <- pp_fun(object, m = m)
   ytilde <- do.call(ppfun, ppargs)
@@ -218,7 +218,7 @@ posterior_predict.stanreg <- function(object, newdata = NULL, draws = NULL,
     ytilde <- matrix(levels(get_y(object))[ytilde], nrow(ytilde), ncol(ytilde))
 
   # fix me!
-  if (is(object, "car")) {
+  if (is_car(object)) {
     if (is.null(newdata)) colnames(ytilde) <- rownames(model.frame(object))
     else colnames(ytilde) <- rownames(newdata)
   }
@@ -440,7 +440,7 @@ pp_eta <- function(object, data, draws = NULL, m = NULL, stanmat = NULL) {
     eta <- t(eta)
   }
 
-  if (is(object, "car")) {
+  if (is_car(object)) {
     psi_indx <- grep("psi", colnames(stanmat))
     psi <- stanmat[, psi_indx, drop = FALSE]
     if (some_draws)
diff --git a/R/stanreg.R b/R/stanreg.R
index 3911c1e08..e44be5e47 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -31,7 +31,7 @@ stanreg <- function(object) {
   nobs <- NROW(y)
   ynames <- if (is.matrix(y)) rownames(y) else names(y)
   
-  is_car <- object$stan_function %in% c("stan_besag", "stan_bym", "stan_bym2")
+  # is_car <- object$stan_function %in% c("stan_besag", "stan_bym", "stan_bym2")
   is_betareg <- is.beta(family$family)
   if (is_betareg) { 
     family_phi <- object$family_phi  # pull out phi family/link
@@ -92,7 +92,7 @@ stanreg <- function(object) {
   
   # linear predictor, fitted values
   eta <- linear_predictor(coefs, x, object$offset)
-  if (is_car) {
+  if (is_car(object)) {
     psi_indx <- grep("psi", colnames(as.matrix(object$stanfit)))
     psi <- as.matrix(object$stanfit)[,psi_indx]
     psi <- unname(colMeans(psi))
@@ -156,7 +156,7 @@ stanreg <- function(object) {
     out$eta_z <- eta_z
     out$phi <- phi
   }
-  if (is_car) {
+  if (is_car(object)) {
     out$psi <- psi
     out$trials <- object$trials
   }

From 557958c44c799d17f15f84ad440906b10744adb5 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sat, 27 Oct 2018 23:53:40 -0400
Subject: [PATCH 53/59] revising spatial model vignette

---
 R/log_lik.R           |  4 ++--
 R/stan_besag.R        |  2 +-
 R/stanreg.R           |  6 +++---
 vignettes/spatial.Rmd | 30 +++++++++++++++++++++++-------
 4 files changed, 29 insertions(+), 13 deletions(-)

diff --git a/R/log_lik.R b/R/log_lik.R
index ae59982fa..5626c71a6 100644
--- a/R/log_lik.R
+++ b/R/log_lik.R
@@ -271,7 +271,7 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
         trials <- 1
         if (is.factor(y))
           y <- fac2bin(y)
-        if (!is(object, "car"))
+        if (!is_car(object))
           stopifnot(all(y %in% c(0, 1)))
         else
           trials <- object$trials
@@ -359,7 +359,7 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
     data <- cbind(data, as.matrix(z)[1:NROW(x),, drop = FALSE])
     draws$beta <- cbind(draws$beta, b)
   }
-  if (is(object, "car")) {
+  if (is_car(object)) {
     psi_indx <- grep("^psi\\[[[:digit:]]+\\]", colnames(stanmat))
     psi <- stanmat[, psi_indx, drop = FALSE]
     data$psi <- t(psi)
diff --git a/R/stan_besag.R b/R/stan_besag.R
index 5b782b79b..29db31bf0 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -151,7 +151,7 @@ stan_besag <- function(formula,
                stan_function = stan_function)
  
   if (family$family == "binomial") {
-    fit$family <- binomial(link = "logit")
+    # fit$family <- binomial(link = "logit")
     fit$trials <- trials
   }
   out <- stanreg(fit)
diff --git a/R/stanreg.R b/R/stanreg.R
index e44be5e47..3911c1e08 100644
--- a/R/stanreg.R
+++ b/R/stanreg.R
@@ -31,7 +31,7 @@ stanreg <- function(object) {
   nobs <- NROW(y)
   ynames <- if (is.matrix(y)) rownames(y) else names(y)
   
-  # is_car <- object$stan_function %in% c("stan_besag", "stan_bym", "stan_bym2")
+  is_car <- object$stan_function %in% c("stan_besag", "stan_bym", "stan_bym2")
   is_betareg <- is.beta(family$family)
   if (is_betareg) { 
     family_phi <- object$family_phi  # pull out phi family/link
@@ -92,7 +92,7 @@ stanreg <- function(object) {
   
   # linear predictor, fitted values
   eta <- linear_predictor(coefs, x, object$offset)
-  if (is_car(object)) {
+  if (is_car) {
     psi_indx <- grep("psi", colnames(as.matrix(object$stanfit)))
     psi <- as.matrix(object$stanfit)[,psi_indx]
     psi <- unname(colMeans(psi))
@@ -156,7 +156,7 @@ stanreg <- function(object) {
     out$eta_z <- eta_z
     out$phi <- phi
   }
-  if (is_car(object)) {
+  if (is_car) {
     out$psi <- psi
     out$trials <- object$trials
   }
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index ba83f2ca4..f2734e293 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -41,9 +41,10 @@ The linear predictor takes the following form,
 $$
 \boldsymbol{\eta} = \alpha + \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi}
 $$
-where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors, $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect (and $N$ denotes the number of spatial units). The construction of $\boldsymbol{\psi}$ depends on the model, which is discussed in the relevant sections below.
+where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors ($N$ being the number of observations and $K$ being the number of predictors), $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect. The construction of $\boldsymbol{\psi}$ depends on the model, which is discussed in the relevant sections below.
+
+Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). With all these components, for some probability density/mass function $f$, we can state the general form of the likelihood as,
 
-Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). Thus, for some probability density/mass function $f$ we can state the general form of the likelihood as,
 $$
 \mathcal{L}(\alpha, \boldsymbol{\beta}, \gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \alpha, \boldsymbol{\beta}, \gamma )
 $$
@@ -51,10 +52,12 @@ $$
 ## GMRF Hierarchical Component
 
 CAR models require that you define the spatial component as a Gaussian Markov Random Field (GMRF). The random vector $\boldsymbol{\phi}$ is a GMRF with respect to the graph $\mathcal{G} = (\mathcal{V} = \{1,\ldots,n\},\mathcal{E})$ with mean vector $\boldsymbol{\mu}$ and precision matrix $\mathbf{W}$ if its probability density function takes the precision form of the multivariate normal distribution,
+
 $$
 f(\boldsymbol{\phi} | \boldsymbol{\mu}) = (2\pi)^{-n/2} |\mathbf{W}|^{1/2}
 \exp\bigg( -\frac{1}{2}(\boldsymbol{\phi-\mu})^{\top}\mathbf{W}(\boldsymbol{\phi-\mu}) \bigg)
 $$
+
 and $W_{i,j} \neq 0$ when $\{i,j\}\in\mathcal{E}$ for all $i \neq j$. Here, $\mathcal{V}$ refers to the verticies on the graph (i.e. the spatial units) and $\mathcal{E}$ refers to the edges on the graph (i.e. the spatial units that are neighbors). In other words, $\mathbf{W}$ is an $N$-by-$N$ matrix  (with zeros on the diagonal) which describes spatial adjacency. 
 
 Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\mathbf{Q} = \mbox{diag}(\mathbf{W1}) - W$ (which is guaranteed to be positive semi-definite) is used as the precision matrix.
@@ -62,6 +65,7 @@ Unfortunately there is no guarantee that $\mathbf{W}$ is positive definite so $\
 ## Besag (ICAR) Spatial Prior
 
 The `stan_besag` modeling function fits the data to an ICAR model. This means that the spatial effect enters the linear predictor as
+
 $$
 \begin{align}
 \boldsymbol{\psi} = \boldsymbol{\phi} \\
@@ -74,7 +78,8 @@ where $\tau$ is a scalar that controls the overall spatial variation and has an
 
 ## BYM
 
-The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which results in misleading parameter estimates and invalid inferences. The `stan_bym` model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial effect takes the following form,
+The ICAR model is limited in that it only accounts for spatial variation among the spatial units. Thus, the random variation is picked up by the spatial variation which may result in misleading parameter estimates and invalid inferences. The `stan_bym` model explains spatial variation as the sum of a structured (spatial) component $\boldsymbol{\phi}$ and an unstructured (random) component $\boldsymbol{\theta}$. Therefore, the spatial effect takes the following form,
+
 $$
 \begin{align}
 \boldsymbol{\psi} &= \rho\boldsymbol{\phi} + \tau\boldsymbol{\theta} \\
@@ -83,11 +88,13 @@ f(\boldsymbol{\phi} | \boldsymbol{\mu}) &= (2\pi)^{-n/2} |\mathbf{Q}|^{1/2}
 f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
 \end{align}
 $$
+
 Note that the unstructured effect $\boldsymbol{\theta}$ is distributed standard normal.
 
 ## BYM2 (Variant of the BYM Spatial Prior)
 
 The `stan_bym2` modeling function fits the data to a variant of the BYM model where the spatial effect enters as a convolution of the structured (spatial) effect and the unstructured (random) effect,
+
 $$
 \begin{align}
 \boldsymbol{\psi} &= \tau(\boldsymbol{\theta}\sqrt{1-\rho} + \boldsymbol{\phi}\sqrt{\rho}) \\
@@ -97,9 +104,9 @@ f(\theta_i) &= (2\pi)^{-1/2}\exp{\bigg( -\frac{\theta_i^2}{2} \bigg)}
 \end{align}
 $$
 
-As in the BYM model $\boldsymbol{\theta}$ is distributed standard normal. However, the parameter $\rho$ is on the unit interval and is interpreted as the proportion of spatial variation that is contributed to overall variation, and $\tau$ explains the overall (convolved) variation.
+As in the BYM model $\boldsymbol{\theta}$ is distributed standard normal. However, now the parameter $\rho$ is on the unit interval and is interpreted as the proportion of spatial variation that is contributed to overall variation, and $\tau$ explains the overall (convolved) variation.
 
-Priors on $\rho$ should be chosen wisely as $\rho=0$ reduces to a model that not account for spatial variation and $\rho=1$ reduces to the ICAR model, which does not account for random variation among the spatial units. 
+Priors on $\rho$ should be chosen carefully as $\rho=0$ reduces to a model that not account for spatial variation and $\rho=1$ reduces to the ICAR model, which does not account for random variation among the spatial units. 
 
 ## Posterior
 
@@ -193,11 +200,19 @@ spplot(grid_sim, "y_pred", at = var_range_y_pred, main = expression(y[pred]),
 ```
 
 Alternatively we can look at the conventional one-dimensional posterior predictive check with the `pp_check` function.
+
 ```{r ppcheck-1d, fig.align='center', fig.height=8}
 pp_check(fit_besag)
 ```
 
-In order to compare the predictive performance between the models we need to use the [loo](http://mc-stan.org/loo) package. Currently, however, this is not supported for CAR spatial models.
+In order to compare the predictive performance between the models we need to use the [loo](http://mc-stan.org/loo) package. Looking at the results below we can confirm that the correct model outperforms the incorrect model in terms of predictive accuracy.
+
+```{r loo}
+library(loo)
+loo_correct <- loo(fit_besag)
+loo_incorrect <- loo(fit_besag_bad)
+compare(loo_correct, loo_incorrect)
+```
 
 ## Smoothing the Spatial Random Walk
 
@@ -205,7 +220,8 @@ In some cases modeling the GMRF spatial component with the precision matrix $\ma
 
 ```{r smoothing, results="hide"}
 fit_besag_smooth <- stan_besag(y ~ 1 + x + z, data = spatial_data, W, order = 2,
-                               prior_intercept = normal(0,1), prior = normal(0,1), prior_rho = normal(0,1),
+                               prior_intercept = normal(0,1), prior = normal(0,1),
+                               prior_rho = normal(0,1),
                                family = binomial(link="logit"), trials = spatial_data$trials,
                                chains = CHAINS, cores = CORES, seed = SEED, iter = ITER)
 ```

From 05eed19ae991cd9dd6aa637e1a08544f931b7d03 Mon Sep 17 00:00:00 2001
From: Ben Goodrich <goodrich.ben@gmail.com>
Date: Sun, 28 Oct 2018 14:08:39 -0400
Subject: [PATCH 54/59] delete src/include

---
 src/include/models.hpp     | 20937 --------------------------------
 src/include/models.hpp.bak | 22874 -----------------------------------
 2 files changed, 43811 deletions(-)
 delete mode 100644 src/include/models.hpp
 delete mode 100644 src/include/models.hpp.bak

diff --git a/src/include/models.hpp b/src/include/models.hpp
deleted file mode 100644
index bc1a4e855..000000000
--- a/src/include/models.hpp
+++ /dev/null
@@ -1,20937 +0,0 @@
-/*
-    rstanarm is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    rstanarm is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with rstanarm.  If not, see <http://www.gnu.org/licenses/>.
-*/
-#ifndef MODELS_HPP
-#define MODELS_HPP
-#define STAN__SERVICES__COMMAND_HPP
-#include <rstan/rstaninc.hpp>
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_bernoulli_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_bernoulli");
-    reader.add_event(760, 760, "end", "model_bernoulli");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    for (int c = 1; c <= r; ++c) {
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            for (int c = 1; c <= nc; ++c) {
-                                for (int r = c; r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            for (int c = 1; c <= nc; ++c) {
-
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    for (int s = 1; s <= nc; ++s) {
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            lp_accum__.add(normal_log(z_b,0,1));
-            lp_accum__.add(normal_log(z_T,0,1));
-            for (int i = 1; i <= t; ++i) {
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            stan::math::assign(nu, (nu - 0.5));
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            stan::math::assign(K, rows(z_beta));
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_gt(t,0))) {
-                for (int j = 1; j <= N; ++j) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_bern(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_bern_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_bern(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type
-ll_bern_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta0,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta1,
-               const int& link,
-               const std::vector<int>& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-
-            lp_accum__.add(logistic_ccdf_log(eta0,0,1));
-            lp_accum__.add(logistic_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,2))) {
-
-            lp_accum__.add(normal_ccdf_log(eta0,0,1));
-            lp_accum__.add(normal_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,3))) {
-
-            lp_accum__.add(cauchy_ccdf_log(eta0,0,1));
-            lp_accum__.add(cauchy_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            lp_accum__.add(log1m_exp(eta0));
-            lp_accum__.add(eta1);
-        } else if (as_bool(logical_eq(link,5))) {
-
-            lp_accum__.add(log1m_exp(minus(exp(eta1))));
-            lp_accum__.add(minus(exp(eta0)));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_bern_lp_functor__ {
-    template <typename T0__, typename T1__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta0,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta1,
-               const int& link,
-               const std::vector<int>& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_bern_lp(eta0, eta1, link, N, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_bern(const int& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,1))) {
-
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), bernoulli_logit_log(y,get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_bern(eta,link, pstream__));
-
-
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), bernoulli_log(y,get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_bern_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const int& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_bern(y, eta, link, pstream__);
-    }
-};
-
-class model_bernoulli : public prob_grad {
-private:
-    int K;
-    vector<int> N;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X0;
-    vector<matrix_d> X1;
-    int nnz_X0;
-    vector_d w_X0;
-    vector<int> v_X0;
-    vector<int> u_X0;
-    int nnz_X1;
-    vector_d w_X1;
-    vector<int> v_X1;
-    vector<int> u_X1;
-    int K_smooth;
-    matrix_d S0;
-    matrix_d S1;
-    vector<int> smooth_map;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights0;
-    vector_d weights1;
-    int has_offset;
-    vector_d offset0;
-    vector_d offset1;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    vector<int> num_non_zero;
-    vector_d w0;
-    vector_d w1;
-    vector<int> v0;
-    vector<int> v1;
-    vector<int> u0;
-    vector<int> u1;
-    int special_case;
-    int NN;
-    double aux;
-    vector<vector<int> > V0;
-    vector<vector<int> > V1;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_bernoulli(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_bernoulli(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_bernoulli_namespace::model_bernoulli";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("N", "2", 2);
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec(2));
-        validate_non_negative_index("N", "2", 2);
-        N = std::vector<int>(2,int(0));
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        size_t N_limit_0__ = 2;
-        for (size_t i_0__ = 0; i_0__ < N_limit_0__; ++i_0__) {
-            N[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X0", "dense_X", dense_X);
-        validate_non_negative_index("X0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("X0", "K", K);
-        context__.validate_dims("data initialization", "X0", "matrix_d", context__.to_vec(dense_X,get_base1(N,1,"N",1),K));
-        validate_non_negative_index("X0", "dense_X", dense_X);
-        validate_non_negative_index("X0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("X0", "K", K);
-        X0 = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X0");
-        pos__ = 0;
-        size_t X0_m_mat_lim__ = get_base1(N,1,"N",1);
-        size_t X0_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X0_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X0_m_mat_lim__; ++m_mat__) {
-                size_t X0_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X0_limit_0__; ++i_0__) {
-                    X0[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        validate_non_negative_index("X1", "dense_X", dense_X);
-        validate_non_negative_index("X1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("X1", "K", K);
-        context__.validate_dims("data initialization", "X1", "matrix_d", context__.to_vec(dense_X,get_base1(N,2,"N",1),K));
-        validate_non_negative_index("X1", "dense_X", dense_X);
-        validate_non_negative_index("X1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("X1", "K", K);
-        X1 = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X1");
-        pos__ = 0;
-        size_t X1_m_mat_lim__ = get_base1(N,2,"N",1);
-        size_t X1_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X1_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X1_m_mat_lim__; ++m_mat__) {
-                size_t X1_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X1_limit_0__; ++i_0__) {
-                    X1[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X0", "int", context__.to_vec());
-        nnz_X0 = int(0);
-        vals_i__ = context__.vals_i("nnz_X0");
-        pos__ = 0;
-        nnz_X0 = vals_i__[pos__++];
-        validate_non_negative_index("w_X0", "nnz_X0", nnz_X0);
-        context__.validate_dims("data initialization", "w_X0", "vector_d", context__.to_vec(nnz_X0));
-        validate_non_negative_index("w_X0", "nnz_X0", nnz_X0);
-        w_X0 = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X0));
-        vals_r__ = context__.vals_r("w_X0");
-        pos__ = 0;
-        size_t w_X0_i_vec_lim__ = nnz_X0;
-        for (size_t i_vec__ = 0; i_vec__ < w_X0_i_vec_lim__; ++i_vec__) {
-            w_X0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X0", "nnz_X0", nnz_X0);
-        context__.validate_dims("data initialization", "v_X0", "int", context__.to_vec(nnz_X0));
-        validate_non_negative_index("v_X0", "nnz_X0", nnz_X0);
-        v_X0 = std::vector<int>(nnz_X0,int(0));
-        vals_i__ = context__.vals_i("v_X0");
-        pos__ = 0;
-        size_t v_X0_limit_0__ = nnz_X0;
-        for (size_t i_0__ = 0; i_0__ < v_X0_limit_0__; ++i_0__) {
-            v_X0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X0", "(dense_X ? 0 : (get_base1(N,1,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ));
-        context__.validate_dims("data initialization", "u_X0", "int", context__.to_vec((dense_X ? 0 : (get_base1(N,1,"N",1) + 1) )));
-        validate_non_negative_index("u_X0", "(dense_X ? 0 : (get_base1(N,1,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ));
-        u_X0 = std::vector<int>((dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X0");
-        pos__ = 0;
-        size_t u_X0_limit_0__ = (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X0_limit_0__; ++i_0__) {
-            u_X0[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "nnz_X1", "int", context__.to_vec());
-        nnz_X1 = int(0);
-        vals_i__ = context__.vals_i("nnz_X1");
-        pos__ = 0;
-        nnz_X1 = vals_i__[pos__++];
-        validate_non_negative_index("w_X1", "nnz_X1", nnz_X1);
-        context__.validate_dims("data initialization", "w_X1", "vector_d", context__.to_vec(nnz_X1));
-        validate_non_negative_index("w_X1", "nnz_X1", nnz_X1);
-        w_X1 = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X1));
-        vals_r__ = context__.vals_r("w_X1");
-        pos__ = 0;
-        size_t w_X1_i_vec_lim__ = nnz_X1;
-        for (size_t i_vec__ = 0; i_vec__ < w_X1_i_vec_lim__; ++i_vec__) {
-            w_X1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X1", "nnz_X1", nnz_X1);
-        context__.validate_dims("data initialization", "v_X1", "int", context__.to_vec(nnz_X1));
-        validate_non_negative_index("v_X1", "nnz_X1", nnz_X1);
-        v_X1 = std::vector<int>(nnz_X1,int(0));
-        vals_i__ = context__.vals_i("v_X1");
-        pos__ = 0;
-        size_t v_X1_limit_0__ = nnz_X1;
-        for (size_t i_0__ = 0; i_0__ < v_X1_limit_0__; ++i_0__) {
-            v_X1[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X1", "(dense_X ? 0 : (get_base1(N,2,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ));
-        context__.validate_dims("data initialization", "u_X1", "int", context__.to_vec((dense_X ? 0 : (get_base1(N,2,"N",1) + 1) )));
-        validate_non_negative_index("u_X1", "(dense_X ? 0 : (get_base1(N,2,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ));
-        u_X1 = std::vector<int>((dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X1");
-        pos__ = 0;
-        size_t u_X1_limit_0__ = (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X1_limit_0__; ++i_0__) {
-            u_X1[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("S0", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S0", "matrix_d", context__.to_vec(get_base1(N,1,"N",1),K_smooth));
-        validate_non_negative_index("S0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("S0", "K_smooth", K_smooth);
-        S0 = matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S0");
-        pos__ = 0;
-        size_t S0_m_mat_lim__ = get_base1(N,1,"N",1);
-        size_t S0_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S0_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S0_m_mat_lim__; ++m_mat__) {
-                S0(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("S1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("S1", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S1", "matrix_d", context__.to_vec(get_base1(N,2,"N",1),K_smooth));
-        validate_non_negative_index("S1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("S1", "K_smooth", K_smooth);
-        S1 = matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S1");
-        pos__ = 0;
-        size_t S1_m_mat_lim__ = get_base1(N,2,"N",1);
-        size_t S1_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S1_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S1_m_mat_lim__; ++m_mat__) {
-                S1(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights0", "(has_weights ? get_base1(N,1,\"N\",1) : 0 )", (has_weights ? get_base1(N,1,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "weights0", "vector_d", context__.to_vec((has_weights ? get_base1(N,1,"N",1) : 0 )));
-        validate_non_negative_index("weights0", "(has_weights ? get_base1(N,1,\"N\",1) : 0 )", (has_weights ? get_base1(N,1,"N",1) : 0 ));
-        weights0 = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? get_base1(N,1,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("weights0");
-        pos__ = 0;
-        size_t weights0_i_vec_lim__ = (has_weights ? get_base1(N,1,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights0_i_vec_lim__; ++i_vec__) {
-            weights0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("weights1", "(has_weights ? get_base1(N,2,\"N\",1) : 0 )", (has_weights ? get_base1(N,2,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "weights1", "vector_d", context__.to_vec((has_weights ? get_base1(N,2,"N",1) : 0 )));
-        validate_non_negative_index("weights1", "(has_weights ? get_base1(N,2,\"N\",1) : 0 )", (has_weights ? get_base1(N,2,"N",1) : 0 ));
-        weights1 = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? get_base1(N,2,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("weights1");
-        pos__ = 0;
-        size_t weights1_i_vec_lim__ = (has_weights ? get_base1(N,2,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights1_i_vec_lim__; ++i_vec__) {
-            weights1[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset0", "(has_offset ? get_base1(N,1,\"N\",1) : 0 )", (has_offset ? get_base1(N,1,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "offset0", "vector_d", context__.to_vec((has_offset ? get_base1(N,1,"N",1) : 0 )));
-        validate_non_negative_index("offset0", "(has_offset ? get_base1(N,1,\"N\",1) : 0 )", (has_offset ? get_base1(N,1,"N",1) : 0 ));
-        offset0 = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? get_base1(N,1,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("offset0");
-        pos__ = 0;
-        size_t offset0_i_vec_lim__ = (has_offset ? get_base1(N,1,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset0_i_vec_lim__; ++i_vec__) {
-            offset0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("offset1", "(has_offset ? get_base1(N,2,\"N\",1) : 0 )", (has_offset ? get_base1(N,2,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "offset1", "vector_d", context__.to_vec((has_offset ? get_base1(N,2,"N",1) : 0 )));
-        validate_non_negative_index("offset1", "(has_offset ? get_base1(N,2,\"N\",1) : 0 )", (has_offset ? get_base1(N,2,"N",1) : 0 ));
-        offset1 = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? get_base1(N,2,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("offset1");
-        pos__ = 0;
-        size_t offset1_i_vec_lim__ = (has_offset ? get_base1(N,2,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset1_i_vec_lim__; ++i_vec__) {
-            offset1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("num_non_zero", "2", 2);
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec(2));
-        validate_non_negative_index("num_non_zero", "2", 2);
-        num_non_zero = std::vector<int>(2,int(0));
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        size_t num_non_zero_limit_0__ = 2;
-        for (size_t i_0__ = 0; i_0__ < num_non_zero_limit_0__; ++i_0__) {
-            num_non_zero[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("w0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        context__.validate_dims("data initialization", "w0", "vector_d", context__.to_vec(get_base1(num_non_zero,1,"num_non_zero",1)));
-        validate_non_negative_index("w0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        w0 = vector_d(static_cast<Eigen::VectorXd::Index>(get_base1(num_non_zero,1,"num_non_zero",1)));
-        vals_r__ = context__.vals_r("w0");
-        pos__ = 0;
-        size_t w0_i_vec_lim__ = get_base1(num_non_zero,1,"num_non_zero",1);
-        for (size_t i_vec__ = 0; i_vec__ < w0_i_vec_lim__; ++i_vec__) {
-            w0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("w1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        context__.validate_dims("data initialization", "w1", "vector_d", context__.to_vec(get_base1(num_non_zero,2,"num_non_zero",1)));
-        validate_non_negative_index("w1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        w1 = vector_d(static_cast<Eigen::VectorXd::Index>(get_base1(num_non_zero,2,"num_non_zero",1)));
-        vals_r__ = context__.vals_r("w1");
-        pos__ = 0;
-        size_t w1_i_vec_lim__ = get_base1(num_non_zero,2,"num_non_zero",1);
-        for (size_t i_vec__ = 0; i_vec__ < w1_i_vec_lim__; ++i_vec__) {
-            w1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        context__.validate_dims("data initialization", "v0", "int", context__.to_vec(get_base1(num_non_zero,1,"num_non_zero",1)));
-        validate_non_negative_index("v0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        v0 = std::vector<int>(get_base1(num_non_zero,1,"num_non_zero",1),int(0));
-        vals_i__ = context__.vals_i("v0");
-        pos__ = 0;
-        size_t v0_limit_0__ = get_base1(num_non_zero,1,"num_non_zero",1);
-        for (size_t i_0__ = 0; i_0__ < v0_limit_0__; ++i_0__) {
-            v0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("v1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        context__.validate_dims("data initialization", "v1", "int", context__.to_vec(get_base1(num_non_zero,2,"num_non_zero",1)));
-        validate_non_negative_index("v1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        v1 = std::vector<int>(get_base1(num_non_zero,2,"num_non_zero",1),int(0));
-        vals_i__ = context__.vals_i("v1");
-        pos__ = 0;
-        size_t v1_limit_0__ = get_base1(num_non_zero,2,"num_non_zero",1);
-        for (size_t i_0__ = 0; i_0__ < v1_limit_0__; ++i_0__) {
-            v1[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u0", "(logical_gt(t,0) ? (get_base1(N,1,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ));
-        context__.validate_dims("data initialization", "u0", "int", context__.to_vec((logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 )));
-        validate_non_negative_index("u0", "(logical_gt(t,0) ? (get_base1(N,1,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ));
-        u0 = std::vector<int>((logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u0");
-        pos__ = 0;
-        size_t u0_limit_0__ = (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u0_limit_0__; ++i_0__) {
-            u0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u1", "(logical_gt(t,0) ? (get_base1(N,2,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ));
-        context__.validate_dims("data initialization", "u1", "int", context__.to_vec((logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 )));
-        validate_non_negative_index("u1", "(logical_gt(t,0) ? (get_base1(N,2,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ));
-        u1 = std::vector<int>((logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u1");
-        pos__ = 0;
-        size_t u1_limit_0__ = (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u1_limit_0__; ++i_0__) {
-            u1[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"K",K,0);
-        for (int k0__ = 0; k0__ < 2; ++k0__) {
-            check_greater_or_equal(function__,"N[k0__]",N[k0__],1);
-        }
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X0",nnz_X0,0);
-        for (int k0__ = 0; k0__ < nnz_X0; ++k0__) {
-            check_greater_or_equal(function__,"v_X0[k0__]",v_X0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X0[k0__]",u_X0[k0__],0);
-        }
-        check_greater_or_equal(function__,"nnz_X1",nnz_X1,0);
-        for (int k0__ = 0; k0__ < nnz_X1; ++k0__) {
-            check_greater_or_equal(function__,"v_X1[k0__]",v_X1[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X1[k0__]",u_X1[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < 2; ++k0__) {
-            check_greater_or_equal(function__,"num_non_zero[k0__]",num_non_zero[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < get_base1(num_non_zero,1,"num_non_zero",1); ++k0__) {
-            check_greater_or_equal(function__,"v0[k0__]",v0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < get_base1(num_non_zero,2,"num_non_zero",1); ++k0__) {
-            check_greater_or_equal(function__,"v1[k0__]",v1[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u0[k0__]",u0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u1[k0__]",u1[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        NN = int(0);
-        stan::math::fill(NN, std::numeric_limits<int>::min());
-        stan::math::assign(NN,(get_base1(N,1,"N",1) + get_base1(N,2,"N",1)));
-        aux = double(0);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::not_a_number());
-        validate_non_negative_index("V0", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        V0 = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(get_base1(N,1,"N",1),int(0)));
-        stan::math::fill(V0, std::numeric_limits<int>::min());
-        stan::math::assign(V0,make_V(get_base1(N,1,"N",1),(special_case ? t : 0 ),v0, pstream__));
-        validate_non_negative_index("V1", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        V1 = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(get_base1(N,2,"N",1),int(0)));
-        stan::math::fill(V1, std::numeric_limits<int>::min());
-        stan::math::assign(V1,make_V(get_base1(N,2,"N",1),(special_case ? t : 0 ),v1, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            if (as_bool(logical_lte(prior_dist,2))) {
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                stan::math::assign(hs, 4);
-            } else {
-                stan::math::assign(hs, 0);
-            }
-            stan::math::assign(pos, 1);
-            for (int i = 1; i <= t; ++i) {
-
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < get_base1(N,1,"N",1); ++k1__) {
-                check_greater_or_equal(function__,"V0[k0__][k1__]",V0[k0__][k1__],1);
-            }
-        }
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < get_base1(N,2,"N",1); ++k1__) {
-                check_greater_or_equal(function__,"V1[k0__][k1__]",V1[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-    }
-
-    ~model_bernoulli() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_ub_unconstrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),lp__));
-            else
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(special_case)) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) eta0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta0, DUMMY_VAR__);
-                stan::math::fill(eta0,DUMMY_VAR__);
-                validate_non_negative_index("eta1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) eta1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta1, DUMMY_VAR__);
-                stan::math::fill(eta1,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-
-                        stan::math::assign(eta0, multiply(get_base1(X0,1,"X0",1),beta));
-                        stan::math::assign(eta1, multiply(get_base1(X1,1,"X1",1),beta));
-                    } else {
-
-                        stan::math::assign(eta0, csr_matrix_times_vector(get_base1(N,1,"N",1),K,w_X0,v_X0,u_X0,beta));
-                        stan::math::assign(eta1, csr_matrix_times_vector(get_base1(N,2,"N",1),K,w_X1,v_X1,u_X1,beta));
-                    }
-                } else {
-
-                    stan::math::assign(eta0, rep_vector(0.0,get_base1(N,1,"N",1)));
-                    stan::math::assign(eta1, rep_vector(0.0,get_base1(N,2,"N",1)));
-                }
-                if (as_bool((primitive_value(logical_eq(has_intercept,0)) && primitive_value(dense_X)))) {
-                    {
-                        T__ tmp;
-                        (void) tmp;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(tmp, DUMMY_VAR__);
-                        stan::math::fill(tmp,DUMMY_VAR__);
-
-
-                        stan::math::assign(tmp, dot_product(xbar,beta));
-                        stan::math::assign(eta0, add(eta0,tmp));
-                        stan::math::assign(eta1, add(eta1,tmp));
-                    }
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-
-                    stan::math::assign(eta0, add(eta0,offset0));
-                    stan::math::assign(eta1, add(eta1,offset1));
-                }
-                if (as_bool(K_smooth)) {
-
-                    stan::math::assign(eta0, add(eta0,multiply(S0,beta_smooth)));
-                    stan::math::assign(eta1, add(eta1,multiply(S1,beta_smooth)));
-                }
-                if (as_bool(special_case)) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(eta0, add(eta0,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V0,i,"V0",1)), stan::model::nil_index_list()), "b")));
-                        stan::math::assign(eta1, add(eta1,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V1,i,"V1",1)), stan::model::nil_index_list()), "b")));
-                    }
-                } else if (as_bool(logical_gt(t,0))) {
-
-                    stan::math::assign(eta0, add(eta0,csr_matrix_times_vector(get_base1(N,1,"N",1),q,w0,v0,u0,b)));
-                    stan::math::assign(eta1, add(eta1,csr_matrix_times_vector(get_base1(N,2,"N",1),q,w1,v1,u1,b)));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_neq(link,4))) {
-
-                        stan::math::assign(eta0, add(get_base1(gamma,1,"gamma",1),eta0));
-                        stan::math::assign(eta1, add(get_base1(gamma,1,"gamma",1),eta1));
-                    } else {
-                        {
-                            T__ shift;
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, DUMMY_VAR__);
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            stan::math::assign(shift, stan::math::fmax(max(eta0),max(eta1)));
-                            stan::math::assign(eta0, subtract(add(get_base1(gamma,1,"gamma",1),eta0),shift));
-                            stan::math::assign(eta1, subtract(add(get_base1(gamma,1,"gamma",1),eta1),shift));
-                        }
-                    }
-                }
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-
-
-                        stan::math::assign(dummy, ll_bern_lp(eta0,eta1,link,N, lp__, lp_accum__, pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-
-                    lp_accum__.add(dot_product(weights0,pw_bern(0,eta0,link, pstream__)));
-                    lp_accum__.add(dot_product(weights1,pw_bern(1,eta1,link, pstream__)));
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                if (as_bool(K_smooth)) {
-
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                if (as_bool(logical_gt(t,0))) {
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_bernoulli_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(special_case)) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                if (as_bool(dense_X)) {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("pi0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                vector_d pi0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) pi0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi0, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi0,DUMMY_VAR__);
-                validate_non_negative_index("pi1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                vector_d pi1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) pi1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi1, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi1,DUMMY_VAR__);
-                validate_non_negative_index("eta0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                vector_d eta0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) eta0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta0, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta0,DUMMY_VAR__);
-                validate_non_negative_index("eta1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                vector_d eta1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) eta1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta1, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta1,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-
-                        stan::math::assign(eta0, multiply(get_base1(X0,1,"X0",1),beta));
-                        stan::math::assign(eta1, multiply(get_base1(X1,1,"X1",1),beta));
-                    } else {
-
-                        stan::math::assign(eta0, csr_matrix_times_vector(get_base1(N,1,"N",1),K,w_X0,v_X0,u_X0,beta));
-                        stan::math::assign(eta1, csr_matrix_times_vector(get_base1(N,2,"N",1),K,w_X1,v_X1,u_X1,beta));
-                    }
-                } else {
-
-                    stan::math::assign(eta0, rep_vector(0.0,get_base1(N,1,"N",1)));
-                    stan::math::assign(eta1, rep_vector(0.0,get_base1(N,2,"N",1)));
-                }
-                if (as_bool((primitive_value(logical_eq(has_intercept,0)) && primitive_value(dense_X)))) {
-                    {
-                        double tmp(0.0);
-                        (void) tmp;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(tmp, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(tmp,DUMMY_VAR__);
-
-
-                        stan::math::assign(tmp, dot_product(xbar,beta));
-                        stan::math::assign(eta0, add(eta0,tmp));
-                        stan::math::assign(eta1, add(eta1,tmp));
-                    }
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-
-                    stan::math::assign(eta0, add(eta0,offset0));
-                    stan::math::assign(eta1, add(eta1,offset1));
-                }
-                if (as_bool(K_smooth)) {
-
-                    stan::math::assign(eta0, add(eta0,multiply(S0,beta_smooth)));
-                    stan::math::assign(eta1, add(eta1,multiply(S1,beta_smooth)));
-                }
-                if (as_bool(special_case)) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(eta0, add(eta0,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V0,i,"V0",1)), stan::model::nil_index_list()), "b")));
-                        stan::math::assign(eta1, add(eta1,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V1,i,"V1",1)), stan::model::nil_index_list()), "b")));
-                    }
-                } else if (as_bool(logical_gt(t,0))) {
-
-                    stan::math::assign(eta0, add(eta0,csr_matrix_times_vector(get_base1(N,1,"N",1),q,w0,v0,u0,b)));
-                    stan::math::assign(eta1, add(eta1,csr_matrix_times_vector(get_base1(N,2,"N",1),q,w1,v1,u1,b)));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_neq(link,4))) {
-
-                        stan::math::assign(eta0, add(get_base1(gamma,1,"gamma",1),eta0));
-                        stan::math::assign(eta1, add(get_base1(gamma,1,"gamma",1),eta1));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            stan::math::assign(shift, stan::math::fmax(max(eta0),max(eta1)));
-                            stan::math::assign(eta0, subtract(add(get_base1(gamma,1,"gamma",1),eta0),shift));
-                            stan::math::assign(eta1, subtract(add(get_base1(gamma,1,"gamma",1),eta1),shift));
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                }
-                stan::math::assign(pi0, linkinv_bern(eta0,link, pstream__));
-                stan::math::assign(pi1, linkinv_bern(eta1,link, pstream__));
-                for (int n = 1; n <= get_base1(N,1,"N",1); ++n) {
-                    stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(get_base1(pi0,n,"pi0",1), base_rng__)));
-                }
-                for (int n = 1; n <= get_base1(N,2,"N",1); ++n) {
-                    stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(get_base1(pi1,n,"pi1",1), base_rng__)));
-                }
-                stan::math::assign(mean_PPD, (mean_PPD / NN));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_bernoulli";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_binomial_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_binomial");
-    reader.add_event(690, 690, "end", "model_binomial");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    for (int c = 1; c <= r; ++c) {
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            for (int c = 1; c <= nc; ++c) {
-                                for (int r = c; r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            for (int c = 1; c <= nc; ++c) {
-
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    for (int s = 1; s <= nc; ++s) {
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            lp_accum__.add(normal_log(z_b,0,1));
-            lp_accum__.add(normal_log(z_T,0,1));
-            for (int i = 1; i <= t; ++i) {
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            stan::math::assign(nu, (nu - 0.5));
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            stan::math::assign(K, rows(z_beta));
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_gt(t,0))) {
-                for (int j = 1; j <= N; ++j) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_binom(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_binom_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_binom(eta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T2__, T_lp__>::type
-ll_binom_lp(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            lp_accum__.add(binomial_logit_log(y,trials,eta));
-        } else if (as_bool(logical_lt(link,4))) {
-            lp_accum__.add(binomial_log(y,trials,linkinv_binom(eta,link, pstream__)));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            for (int n = 1; n <= num_elements(y); ++n) {
-
-                lp_accum__.add((get_base1(y,n,"y",1) * get_base1(eta,n,"eta",1)));
-                lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * log1m_exp(get_base1(eta,n,"eta",1))));
-                lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-            }
-        } else if (as_bool(logical_eq(link,5))) {
-
-            for (int n = 1; n <= num_elements(y); ++n) {
-                {
-                    fun_scalar_t__ neg_exp_eta;
-                    (void) neg_exp_eta;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(neg_exp_eta, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(neg_exp_eta,DUMMY_VAR__);
-                    stan::math::assign(neg_exp_eta,-(exp(get_base1(eta,n,"eta",1))));
-
-
-                    lp_accum__.add((get_base1(y,n,"y",1) * log1m_exp(neg_exp_eta)));
-                    lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * neg_exp_eta));
-                    lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-                }
-            }
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_binom_lp_functor__ {
-    template <typename T2__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T2__, T_lp__>::type
-    operator()(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_binom_lp(y, trials, eta, link, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-pw_binom(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,1))) {
-
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_logit_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_binom(eta,link, pstream__));
-
-
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_binom_functor__ {
-    template <typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) const {
-        return pw_binom(y, trials, eta, link, pstream__);
-    }
-};
-
-class model_binomial : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    vector<int> y;
-    vector<int> trials;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    double aux;
-    vector<vector<int> > V;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_binomial(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_binomial(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_binomial_namespace::model_binomial";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("trials", "N", N);
-        context__.validate_dims("data initialization", "trials", "int", context__.to_vec(N));
-        validate_non_negative_index("trials", "N", N);
-        trials = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("trials");
-        pos__ = 0;
-        size_t trials_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < trials_limit_0__; ++i_0__) {
-            trials[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"trials[k0__]",trials[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        aux = double(0);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::not_a_number());
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "N", N);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(N,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(N,(special_case ? t : 0 ),v, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            if (as_bool(logical_lte(prior_dist,2))) {
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                stan::math::assign(hs, 4);
-            } else {
-                stan::math::assign(hs, 0);
-            }
-            stan::math::assign(pos, 1);
-            for (int i = 1; i <= t; ++i) {
-
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < N; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-    }
-
-    ~model_binomial() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_ub_unconstrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),lp__));
-            else
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_neq(link,4))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        stan::math::assign(eta, subtract(add(get_base1(gamma,1,"gamma",1),eta),max(eta)));
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-
-
-                        stan::math::assign(dummy, ll_binom_lp(y,trials,eta,link, lp__, lp_accum__, pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    lp_accum__.add(dot_product(weights,pw_binom(y,trials,eta,link, pstream__)));
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                if (as_bool(K_smooth)) {
-
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                if (as_bool(logical_gt(t,0))) {
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_binomial_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                if (as_bool(dense_X)) {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("pi", "N", N);
-                vector_d pi(static_cast<Eigen::VectorXd::Index>(N));
-                (void) pi;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_neq(link,4))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            stan::math::assign(shift, max(eta));
-                            stan::math::assign(eta, subtract(add(get_base1(gamma,1,"gamma",1),eta),shift));
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                stan::math::assign(pi, linkinv_binom(eta,link, pstream__));
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(mean_PPD, (mean_PPD + binomial_rng(get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1), base_rng__)));
-                }
-                stan::math::assign(mean_PPD, (mean_PPD / N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_binomial";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_continuous_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_continuous");
-    reader.add_event(1369, 1369, "end", "model_continuous");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    for (int c = 1; c <= r; ++c) {
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            for (int c = 1; c <= nc; ++c) {
-                                for (int r = c; r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            for (int c = 1; c <= nc; ++c) {
-
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    for (int s = 1; s <= nc; ++s) {
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            lp_accum__.add(normal_log(z_b,0,1));
-            lp_accum__.add(normal_log(z_T,0,1));
-            for (int i = 1; i <= t; ++i) {
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            stan::math::assign(nu, (nu - 0.5));
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            stan::math::assign(K, rows(z_beta));
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_gt(t,0))) {
-                for (int j = 1; j <= N; ++j) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gauss_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gauss(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(subtract((-(0.5) * log((6.2831853071795862 * sigma))),multiply(0.5,square(divide(subtract(y,linkinv_gauss(eta,link, pstream__)),sigma)))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gauss_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gauss(y, eta, sigma, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gamma_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gamma(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-GammaReg(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ ret;
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-            stan::math::assign(ret,((rows(y) * ((shape * log(shape)) - stan::math::lgamma(shape))) + ((shape - 1) * sum_log_y)));
-
-
-            if (as_bool(logical_eq(link,2))) {
-                stan::math::assign(ret, ((ret - (shape * sum(eta))) - (shape * sum(elt_divide(y,exp(eta))))));
-            } else if (as_bool(logical_eq(link,1))) {
-                stan::math::assign(ret, ((ret - (shape * sum(log(eta)))) - (shape * sum(elt_divide(y,eta)))));
-            } else if (as_bool(logical_eq(link,3))) {
-                stan::math::assign(ret, ((ret + (shape * sum(log(eta)))) - (shape * dot_product(eta,y))));
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct GammaReg_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) const {
-        return GammaReg(y, eta, shape, link, sum_log_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,3))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape * get_base1(eta,n,"eta",1))));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / exp(get_base1(eta,n,"eta",1)))));
-                }
-            } else if (as_bool(logical_eq(link,1))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / get_base1(eta,n,"eta",1))));
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gamma_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gamma(y, eta, shape, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_sqrt(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_inv_gaussian_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) const {
-        return linkinv_inv_gaussian(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(((((0.5 * rows(y)) * log((lambda / 6.2831853071795862))) - (1.5 * sum_log_y)) - ((0.5 * lambda) * dot_self(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y))))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return inv_gaussian(y, mu, lambda, sum_log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-pw_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_inv_gaussian(eta,link, pstream__));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(add(multiply((-(0.5) * lambda),square(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y)))),(0.5 * log((lambda / 6.2831853071795862)))),multiply(1.5,log_y)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return pw_inv_gaussian(y, eta, lambda, link, log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-inv_gaussian_rng(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ mu2;
-            (void) mu2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu2,DUMMY_VAR__);
-            stan::math::assign(mu2,square(mu));
-            fun_scalar_t__ z;
-            (void) z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z,DUMMY_VAR__);
-            stan::math::assign(z,uniform_rng(0,1, base_rng__));
-            fun_scalar_t__ y;
-            (void) y;  // dummy to suppress unused var warning
-
-            stan::math::initialize(y, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(y,DUMMY_VAR__);
-            stan::math::assign(y,square(normal_rng(0,1, base_rng__)));
-            fun_scalar_t__ x;
-            (void) x;  // dummy to suppress unused var warning
-
-            stan::math::initialize(x, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(x,DUMMY_VAR__);
-            stan::math::assign(x,(mu + (((mu2 * y) - (mu * sqrt(((((4 * mu) * lambda) * y) + (mu2 * square(y)))))) / (2 * lambda))));
-
-
-            if (as_bool(logical_lte(z,(mu / (mu + x))))) {
-                return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-            } else {
-                return stan::math::promote_scalar<fun_return_scalar_t__>((mu2 / x));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_rng_functor__ {
-    template <typename T0__, typename T1__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) const {
-        return inv_gaussian_rng(mu, lambda, base_rng__, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(0.5,divide(atan(eta),stan::math::pi())));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,6))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(1,inv_cloglog(minus(eta))));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_beta(eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_z_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) const {
-        return linkinv_beta_z(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-
-
-            for (int n = 1; n <= rows(y); ++n) {
-
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * dispersion),((1 - get_base1(mu,n,"mu",1)) * dispersion)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) const {
-        return pw_beta(y, eta, dispersion, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-            validate_non_negative_index("mu_z", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu_z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu_z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu_z,DUMMY_VAR__);
-            stan::math::assign(mu_z,linkinv_beta_z(eta_z,link_phi, pstream__));
-
-
-            for (int n = 1; n <= rows(y); ++n) {
-
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * get_base1(mu_z,n,"mu_z",1)),((1 - get_base1(mu,n,"mu",1)) * get_base1(mu_z,n,"mu_z",1))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_z_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) const {
-        return pw_beta_z(y, eta, eta_z, link, link_phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asymp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOff(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOrig(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_biexp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-SS_fol(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_fpl(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_gompertz(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_logis(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_micmen(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_weibull(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asymp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            {
-                validate_non_negative_index("Asym", "rows(Phi)", rows(Phi));
-                Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  Asym(static_cast<Eigen::VectorXd::Index>(rows(Phi)));
-                (void) Asym;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Asym,DUMMY_VAR__);
-                stan::math::assign(Asym,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-
-
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(Asym,elt_multiply(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),Asym),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"))),input)))));
-            }
-        } else {
-            {
-                fun_scalar_t__ Asym;
-                (void) Asym;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Asym,DUMMY_VAR__);
-                stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-
-
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(Asym,multiply((get_base1(Phi,1,2,"Phi",1) - Asym),exp(multiply(-(exp(get_base1(Phi,1,3,"Phi",1))),input)))));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asymp_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asymp(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOff(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),subtract(1,exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),subtract(input,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))))));
-        } else {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(get_base1(Phi,1,1,"Phi",1),subtract(1,exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),subtract(input,get_base1(Phi,1,3,"Phi",1)))))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asympOff_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asympOff(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOrig(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),subtract(1,exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),input)))));
-        } else {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(get_base1(Phi,1,1,"Phi",1),subtract(1,exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),input)))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asympOrig_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asympOrig(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_biexp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),input))),elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(4), stan::model::nil_index_list())), "Phi"))),input)))));
-        } else {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(multiply(get_base1(Phi,1,1,"Phi",1),exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),input))),multiply(get_base1(Phi,1,3,"Phi",1),exp(multiply(-(exp(get_base1(Phi,1,4,"Phi",1))),input)))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_biexp_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_biexp(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-SS_fol(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int Phi_rows(0);
-            (void) Phi_rows;  // dummy to suppress unused var warning
-
-            stan::math::fill(Phi_rows, std::numeric_limits<int>::min());
-            stan::math::assign(Phi_rows,rows(Phi));
-
-
-            if (as_bool(logical_gt(Phi_rows,1))) {
-                {
-                    validate_non_negative_index("lKe", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lKe(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKe,DUMMY_VAR__);
-                    stan::math::assign(lKe,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-                    validate_non_negative_index("lKa", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lKa(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKa,DUMMY_VAR__);
-                    stan::math::assign(lKa,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"));
-                    validate_non_negative_index("exp_lKe", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  exp_lKe(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) exp_lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKe,DUMMY_VAR__);
-                    stan::math::assign(exp_lKe,exp(lKe));
-                    validate_non_negative_index("exp_lKa", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  exp_lKa(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) exp_lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKa,DUMMY_VAR__);
-                    stan::math::assign(exp_lKa,exp(lKa));
-
-
-                    return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(elt_multiply(elt_multiply(Dose,exp(subtract(add(lKe,lKa),stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))),subtract(exp(elt_multiply(minus(exp_lKe),input)),exp(elt_multiply(minus(exp_lKa),input)))),subtract(exp_lKa,exp_lKe)));
-                }
-            } else {
-                {
-                    fun_scalar_t__ lKe;
-                    (void) lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKe,DUMMY_VAR__);
-                    stan::math::assign(lKe,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ lKa;
-                    (void) lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKa,DUMMY_VAR__);
-                    stan::math::assign(lKa,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ exp_lKe;
-                    (void) exp_lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKe,DUMMY_VAR__);
-                    stan::math::assign(exp_lKe,exp(lKe));
-                    fun_scalar_t__ exp_lKa;
-                    (void) exp_lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKa,DUMMY_VAR__);
-                    stan::math::assign(exp_lKa,exp(lKa));
-
-
-                    return stan::math::promote_scalar<fun_return_scalar_t__>(divide(elt_multiply(multiply(Dose,exp(((lKe + lKa) - get_base1(Phi,1,3,"Phi",1)))),subtract(exp(multiply(-(exp_lKe),input)),exp(multiply(-(exp_lKa),input)))),(exp_lKa - exp_lKe)));
-                }
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_fol_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_fol(Dose, input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_fpl(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            {
-                validate_non_negative_index("A", "rows(Phi)", rows(Phi));
-                Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  A(static_cast<Eigen::VectorXd::Index>(rows(Phi)));
-                (void) A;  // dummy to suppress unused var warning
-
-                stan::math::initialize(A, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(A,DUMMY_VAR__);
-                stan::math::assign(A,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-
-
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(A,elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),A),add(1,exp(elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"),input),exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(4), stan::model::nil_index_list())), "Phi"))))))));
-            }
-        } else {
-            {
-                fun_scalar_t__ A;
-                (void) A;  // dummy to suppress unused var warning
-
-                stan::math::initialize(A, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(A,DUMMY_VAR__);
-                stan::math::assign(A,get_base1(Phi,1,1,"Phi",1));
-
-
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(A,elt_divide(rep_vector((get_base1(Phi,1,2,"Phi",1) - A),rows(input)),add(1,exp(divide(subtract(get_base1(Phi,1,3,"Phi",1),input),exp(get_base1(Phi,1,4,"Phi",1))))))));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_fpl_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_fpl(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_gompertz(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "rows(x)", rows(x));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  out(static_cast<Eigen::VectorXd::Index>(rows(x)));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-
-
-            if (as_bool(logical_gt(rows(Phi),1))) {
-                for (int i = 1; i <= rows(x); ++i) {
-                    stan::math::assign(get_base1_lhs(out,i,"out",1), (get_base1(Phi,i,1,"Phi",1) * exp((-(get_base1(Phi,i,2,"Phi",1)) * pow(get_base1(Phi,i,3,"Phi",1),get_base1(x,i,"x",1))))));
-                }
-            } else {
-                {
-                    fun_scalar_t__ Asym;
-                    (void) Asym;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Asym,DUMMY_VAR__);
-                    stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ b2;
-                    (void) b2;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(b2, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(b2,DUMMY_VAR__);
-                    stan::math::assign(b2,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ b3;
-                    (void) b3;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(b3, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(b3,DUMMY_VAR__);
-                    stan::math::assign(b3,get_base1(Phi,1,3,"Phi",1));
-
-
-                    for (int i = 1; i <= rows(x); ++i) {
-                        stan::math::assign(get_base1_lhs(out,i,"out",1), (Asym * exp((-(b2) * pow(b3,get_base1(x,i,"x",1))))));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_gompertz_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_gompertz(x, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_logis(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),add(1,exp(elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),input),exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))))));
-        } else {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(rep_vector(get_base1(Phi,1,1,"Phi",1),rows(input)),add(1,exp(divide(subtract(get_base1(Phi,1,2,"Phi",1),input),exp(get_base1(Phi,1,3,"Phi",1)))))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_logis_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_logis(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_micmen(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),input),add(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),input)));
-        } else {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(multiply(get_base1(Phi,1,1,"Phi",1),input),add(get_base1(Phi,1,2,"Phi",1),input)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_micmen_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_micmen(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_weibull(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "rows(x)", rows(x));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  out(static_cast<Eigen::VectorXd::Index>(rows(x)));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-
-
-            if (as_bool(logical_gt(rows(Phi),1))) {
-                for (int i = 1; i <= rows(x); ++i) {
-                    stan::math::assign(get_base1_lhs(out,i,"out",1), (get_base1(Phi,i,1,"Phi",1) - (get_base1(Phi,i,2,"Phi",1) * exp((-(exp(get_base1(Phi,i,3,"Phi",1))) * pow(get_base1(x,i,"x",1),get_base1(Phi,i,4,"Phi",1)))))));
-                }
-            } else {
-                {
-                    fun_scalar_t__ Asym;
-                    (void) Asym;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Asym,DUMMY_VAR__);
-                    stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ Drop;
-                    (void) Drop;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Drop, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Drop,DUMMY_VAR__);
-                    stan::math::assign(Drop,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ lrc;
-                    (void) lrc;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lrc, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lrc,DUMMY_VAR__);
-                    stan::math::assign(lrc,get_base1(Phi,1,3,"Phi",1));
-                    fun_scalar_t__ pwr;
-                    (void) pwr;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pwr, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pwr,DUMMY_VAR__);
-                    stan::math::assign(pwr,get_base1(Phi,1,4,"Phi",1));
-
-
-                    for (int i = 1; i <= rows(x); ++i) {
-                        stan::math::assign(get_base1_lhs(out,i,"out",1), (Asym - (Drop * exp((-(exp(lrc)) * pow(get_base1(x,i,"x",1),pwr))))));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_weibull_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_weibull(x, Phi, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,Eigen::Dynamic>
-reshape_vec(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const int& Rows,
-                const int& Cols, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "Rows", Rows);
-            validate_non_negative_index("out", "Cols", Cols);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  out(static_cast<Eigen::VectorXd::Index>(Rows),static_cast<Eigen::VectorXd::Index>(Cols));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_neq(rows(x),(Rows * Cols)))) {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "x is the wrong length";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            for (int c = 1; c <= Cols; ++c) {
-                for (int r = 1; r <= Rows; ++r) {
-
-                    stan::math::assign(get_base1_lhs(out,r,c,"out",1), get_base1(x,pos,"x",1));
-                    stan::math::assign(pos, (pos + 1));
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct reshape_vec_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,Eigen::Dynamic>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const int& Rows,
-                const int& Cols, std::ostream* pstream__) const {
-        return reshape_vec(x, Rows, Cols, pstream__);
-    }
-};
-
-double
-make_lower(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(family,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-        }
-        if (as_bool(logical_lte(family,3))) {
-
-            if (as_bool(logical_eq(link,2))) {
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_lower_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_lower(family, link, pstream__);
-    }
-};
-
-double
-make_upper(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::positive_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_upper_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_upper(family, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T5__>::type, Eigen::Dynamic,1>
-test_csr_matrix_times_vector(const int& m,
-                                 const int& n,
-                                 const Eigen::Matrix<T2__, Eigen::Dynamic,1>& w,
-                                 const std::vector<int>& v,
-                                 const std::vector<int>& u,
-                                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& b, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T5__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(csr_matrix_times_vector(m,n,w,v,u,b));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct test_csr_matrix_times_vector_functor__ {
-    template <typename T2__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T5__>::type, Eigen::Dynamic,1>
-    operator()(const int& m,
-                                 const int& n,
-                                 const Eigen::Matrix<T2__, Eigen::Dynamic,1>& w,
-                                 const std::vector<int>& v,
-                                 const std::vector<int>& u,
-                                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& b, std::ostream* pstream__) const {
-        return test_csr_matrix_times_vector(m, n, w, v, u, b, pstream__);
-    }
-};
-
-class model_continuous : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    int len_y;
-    double lb_y;
-    double ub_y;
-    vector_d y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    int has_intercept_z;
-    int link_phi;
-    int z_dim;
-    matrix_d betareg_z;
-    row_vector_d zbar;
-    int prior_dist_z;
-    int prior_dist_for_intercept_z;
-    vector_d prior_scale_z;
-    double prior_scale_for_intercept_z;
-    vector_d prior_mean_z;
-    double prior_mean_for_intercept_z;
-    vector_d prior_df_z;
-    double prior_df_for_intercept_z;
-    double global_prior_scale_z;
-    vector<int> num_normals_z;
-    int SSfun;
-    vector_d input;
-    vector_d Dose;
-    vector_d sqrt_y;
-    vector_d log_y;
-    double sum_log_y;
-    vector<vector<int> > V;
-    int hs_z;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_continuous(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_continuous(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_continuous_namespace::model_continuous";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_y", "int", context__.to_vec());
-        len_y = int(0);
-        vals_i__ = context__.vals_i("len_y");
-        pos__ = 0;
-        len_y = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "lb_y", "double", context__.to_vec());
-        lb_y = double(0);
-        vals_r__ = context__.vals_r("lb_y");
-        pos__ = 0;
-        lb_y = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "ub_y", "double", context__.to_vec());
-        ub_y = double(0);
-        vals_r__ = context__.vals_r("ub_y");
-        pos__ = 0;
-        ub_y = vals_r__[pos__++];
-        validate_non_negative_index("y", "len_y", len_y);
-        context__.validate_dims("data initialization", "y", "vector_d", context__.to_vec(len_y));
-        validate_non_negative_index("y", "len_y", len_y);
-        y = vector_d(static_cast<Eigen::VectorXd::Index>(len_y));
-        vals_r__ = context__.vals_r("y");
-        pos__ = 0;
-        size_t y_i_vec_lim__ = len_y;
-        for (size_t i_vec__ = 0; i_vec__ < y_i_vec_lim__; ++i_vec__) {
-            y[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept_z", "int", context__.to_vec());
-        has_intercept_z = int(0);
-        vals_i__ = context__.vals_i("has_intercept_z");
-        pos__ = 0;
-        has_intercept_z = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link_phi", "int", context__.to_vec());
-        link_phi = int(0);
-        vals_i__ = context__.vals_i("link_phi");
-        pos__ = 0;
-        link_phi = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "z_dim", "int", context__.to_vec());
-        z_dim = int(0);
-        vals_i__ = context__.vals_i("z_dim");
-        pos__ = 0;
-        z_dim = vals_i__[pos__++];
-        validate_non_negative_index("betareg_z", "N", N);
-        validate_non_negative_index("betareg_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "betareg_z", "matrix_d", context__.to_vec(N,z_dim));
-        validate_non_negative_index("betareg_z", "N", N);
-        validate_non_negative_index("betareg_z", "z_dim", z_dim);
-        betareg_z = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("betareg_z");
-        pos__ = 0;
-        size_t betareg_z_m_mat_lim__ = N;
-        size_t betareg_z_n_mat_lim__ = z_dim;
-        for (size_t n_mat__ = 0; n_mat__ < betareg_z_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < betareg_z_m_mat_lim__; ++m_mat__) {
-                betareg_z(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("zbar", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "zbar", "row_vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("zbar", "z_dim", z_dim);
-        zbar = row_vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("zbar");
-        pos__ = 0;
-        size_t zbar_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < zbar_i_vec_lim__; ++i_vec__) {
-            zbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_z", "int", context__.to_vec());
-        prior_dist_z = int(0);
-        vals_i__ = context__.vals_i("prior_dist_z");
-        pos__ = 0;
-        prior_dist_z = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept_z", "int", context__.to_vec());
-        prior_dist_for_intercept_z = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept_z");
-        pos__ = 0;
-        prior_dist_for_intercept_z = vals_i__[pos__++];
-        validate_non_negative_index("prior_scale_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_scale_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_scale_z", "z_dim", z_dim);
-        prior_scale_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_scale_z");
-        pos__ = 0;
-        size_t prior_scale_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_z_i_vec_lim__; ++i_vec__) {
-            prior_scale_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept_z", "double", context__.to_vec());
-        prior_scale_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept_z");
-        pos__ = 0;
-        prior_scale_for_intercept_z = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_mean_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_mean_z", "z_dim", z_dim);
-        prior_mean_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_mean_z");
-        pos__ = 0;
-        size_t prior_mean_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_z_i_vec_lim__; ++i_vec__) {
-            prior_mean_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept_z", "double", context__.to_vec());
-        prior_mean_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept_z");
-        pos__ = 0;
-        prior_mean_for_intercept_z = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_df_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_df_z", "z_dim", z_dim);
-        prior_df_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_df_z");
-        pos__ = 0;
-        size_t prior_df_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_z_i_vec_lim__; ++i_vec__) {
-            prior_df_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept_z", "double", context__.to_vec());
-        prior_df_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept_z");
-        pos__ = 0;
-        prior_df_for_intercept_z = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale_z", "double", context__.to_vec());
-        global_prior_scale_z = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale_z");
-        pos__ = 0;
-        global_prior_scale_z = vals_r__[pos__++];
-        validate_non_negative_index("num_normals_z", "(logical_eq(prior_dist_z,7) ? z_dim : 0 )", (logical_eq(prior_dist_z,7) ? z_dim : 0 ));
-        context__.validate_dims("data initialization", "num_normals_z", "int", context__.to_vec((logical_eq(prior_dist_z,7) ? z_dim : 0 )));
-        validate_non_negative_index("num_normals_z", "(logical_eq(prior_dist_z,7) ? z_dim : 0 )", (logical_eq(prior_dist_z,7) ? z_dim : 0 ));
-        num_normals_z = std::vector<int>((logical_eq(prior_dist_z,7) ? z_dim : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals_z");
-        pos__ = 0;
-        size_t num_normals_z_limit_0__ = (logical_eq(prior_dist_z,7) ? z_dim : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_z_limit_0__; ++i_0__) {
-            num_normals_z[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "SSfun", "int", context__.to_vec());
-        SSfun = int(0);
-        vals_i__ = context__.vals_i("SSfun");
-        pos__ = 0;
-        SSfun = vals_i__[pos__++];
-        validate_non_negative_index("input", "(logical_gt(SSfun,0) ? len_y : 0 )", (logical_gt(SSfun,0) ? len_y : 0 ));
-        context__.validate_dims("data initialization", "input", "vector_d", context__.to_vec((logical_gt(SSfun,0) ? len_y : 0 )));
-        validate_non_negative_index("input", "(logical_gt(SSfun,0) ? len_y : 0 )", (logical_gt(SSfun,0) ? len_y : 0 ));
-        input = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(SSfun,0) ? len_y : 0 )));
-        vals_r__ = context__.vals_r("input");
-        pos__ = 0;
-        size_t input_i_vec_lim__ = (logical_gt(SSfun,0) ? len_y : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < input_i_vec_lim__; ++i_vec__) {
-            input[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("Dose", "(logical_eq(SSfun,5) ? len_y : 0 )", (logical_eq(SSfun,5) ? len_y : 0 ));
-        context__.validate_dims("data initialization", "Dose", "vector_d", context__.to_vec((logical_eq(SSfun,5) ? len_y : 0 )));
-        validate_non_negative_index("Dose", "(logical_eq(SSfun,5) ? len_y : 0 )", (logical_eq(SSfun,5) ? len_y : 0 ));
-        Dose = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(SSfun,5) ? len_y : 0 )));
-        vals_r__ = context__.vals_r("Dose");
-        pos__ = 0;
-        size_t Dose_i_vec_lim__ = (logical_eq(SSfun,5) ? len_y : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < Dose_i_vec_lim__; ++i_vec__) {
-            Dose[i_vec__] = vals_r__[pos__++];
-        }
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"len_y",len_y,0);
-        check_greater_or_equal(function__,"ub_y",ub_y,lb_y);
-        check_greater_or_equal(function__,"y",y,lb_y);
-        check_less_or_equal(function__,"y",y,ub_y);
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        check_greater_or_equal(function__,"has_intercept_z",has_intercept_z,0);
-        check_less_or_equal(function__,"has_intercept_z",has_intercept_z,1);
-        check_greater_or_equal(function__,"link_phi",link_phi,0);
-        check_greater_or_equal(function__,"z_dim",z_dim,0);
-        check_greater_or_equal(function__,"prior_dist_z",prior_dist_z,0);
-        check_less_or_equal(function__,"prior_dist_z",prior_dist_z,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept_z",prior_dist_for_intercept_z,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept_z",prior_dist_for_intercept_z,2);
-        check_greater_or_equal(function__,"prior_scale_z",prior_scale_z,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept_z",prior_scale_for_intercept_z,0);
-        check_greater_or_equal(function__,"prior_df_z",prior_df_z,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept_z",prior_df_for_intercept_z,0);
-        check_greater_or_equal(function__,"global_prior_scale_z",global_prior_scale_z,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist_z,7) ? z_dim : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals_z[k0__]",num_normals_z[k0__],2);
-        }
-        check_greater_or_equal(function__,"SSfun",SSfun,0);
-        check_less_or_equal(function__,"SSfun",SSfun,10);
-        // initialize data variables
-        validate_non_negative_index("sqrt_y", "(logical_eq(family,3) ? len_y : 0 )", (logical_eq(family,3) ? len_y : 0 ));
-        sqrt_y = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(family,3) ? len_y : 0 )));
-        stan::math::fill(sqrt_y,DUMMY_VAR__);
-        validate_non_negative_index("log_y", "(logical_eq(family,3) ? len_y : 0 )", (logical_eq(family,3) ? len_y : 0 ));
-        log_y = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(family,3) ? len_y : 0 )));
-        stan::math::fill(log_y,DUMMY_VAR__);
-        sum_log_y = double(0);
-        stan::math::fill(sum_log_y,DUMMY_VAR__);
-        stan::math::assign(sum_log_y,(logical_eq(family,1) ? stan::math::not_a_number() : sum(log(y)) ));
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "len_y", len_y);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(len_y,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(len_y,(special_case ? t : 0 ),v, pstream__));
-        hs_z = int(0);
-        stan::math::fill(hs_z, std::numeric_limits<int>::min());
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            if (as_bool(logical_lte(prior_dist,2))) {
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                stan::math::assign(hs, 4);
-            } else {
-                stan::math::assign(hs, 0);
-            }
-            stan::math::assign(pos, 1);
-            for (int i = 1; i <= t; ++i) {
-
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-            if (as_bool(logical_lte(prior_dist_z,2))) {
-                stan::math::assign(hs_z, 0);
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                stan::math::assign(hs_z, 2);
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                stan::math::assign(hs_z, 4);
-            } else {
-                stan::math::assign(hs_z, 0);
-            }
-            stan::math::assign(is_continuous, 1);
-            if (as_bool(logical_eq(family,3))) {
-
-                stan::math::assign(sqrt_y, sqrt(y));
-                stan::math::assign(log_y, log(y));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < len_y; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"hs_z",hs_z,0);
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-        ++num_params_r__;
-        validate_non_negative_index("z_omega", "(logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )", (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        num_params_r__ += (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim );
-        validate_non_negative_index("gamma_z", "has_intercept_z", has_intercept_z);
-        num_params_r__ += has_intercept_z;
-        validate_non_negative_index("global_z", "hs_z", hs_z);
-        num_params_r__ += hs_z;
-        validate_non_negative_index("local_z", "z_dim", z_dim);
-        validate_non_negative_index("local_z", "hs_z", hs_z);
-        num_params_r__ += z_dim * hs_z;
-        validate_non_negative_index("S_z", "z_dim", z_dim);
-        validate_non_negative_index("S_z", "(primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)))", (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        num_params_r__ += z_dim * (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        validate_non_negative_index("one_over_lambda_z", "logical_eq(prior_dist_z,6)", logical_eq(prior_dist_z,6));
-        num_params_r__ += logical_eq(prior_dist_z,6);
-    }
-
-    ~model_continuous() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec());
-        // generate_declaration aux_unscaled
-        double aux_unscaled(0);
-        aux_unscaled = vals_r__[pos__++];
-        try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_omega")))
-            throw std::runtime_error("variable z_omega missing");
-        vals_r__ = context__.vals_r("z_omega");
-        pos__ = 0U;
-        validate_non_negative_index("z_omega", "(logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )", (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        context__.validate_dims("initialization", "z_omega", "vector_d", context__.to_vec((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )));
-        // generate_declaration z_omega
-        vector_d z_omega(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++j1__)
-            z_omega(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_omega);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_omega: ") + e.what());
-        }
-
-        if (!(context__.contains_r("gamma_z")))
-            throw std::runtime_error("variable gamma_z missing");
-        vals_r__ = context__.vals_r("gamma_z");
-        pos__ = 0U;
-        validate_non_negative_index("gamma_z", "has_intercept_z", has_intercept_z);
-        context__.validate_dims("initialization", "gamma_z", "double", context__.to_vec(has_intercept_z));
-        // generate_declaration gamma_z
-        std::vector<double> gamma_z(has_intercept_z,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept_z; ++i0__)
-            gamma_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept_z; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) ),gamma_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global_z")))
-            throw std::runtime_error("variable global_z missing");
-        vals_r__ = context__.vals_r("global_z");
-        pos__ = 0U;
-        validate_non_negative_index("global_z", "hs_z", hs_z);
-        context__.validate_dims("initialization", "global_z", "double", context__.to_vec(hs_z));
-        // generate_declaration global_z
-        std::vector<double> global_z(hs_z,double(0));
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            global_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local_z")))
-            throw std::runtime_error("variable local_z missing");
-        vals_r__ = context__.vals_r("local_z");
-        pos__ = 0U;
-        validate_non_negative_index("local_z", "hs_z", hs_z);
-        validate_non_negative_index("local_z", "z_dim", z_dim);
-        context__.validate_dims("initialization", "local_z", "vector_d", context__.to_vec(hs_z,z_dim));
-        // generate_declaration local_z
-        std::vector<vector_d> local_z(hs_z,vector_d(static_cast<Eigen::VectorXd::Index>(z_dim)));
-        for (int j1__ = 0U; j1__ < z_dim; ++j1__)
-            for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-                local_z[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("S_z")))
-            throw std::runtime_error("variable S_z missing");
-        vals_r__ = context__.vals_r("S_z");
-        pos__ = 0U;
-        validate_non_negative_index("S_z", "(primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)))", (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        validate_non_negative_index("S_z", "z_dim", z_dim);
-        context__.validate_dims("initialization", "S_z", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))),z_dim));
-        // generate_declaration S_z
-        std::vector<vector_d> S_z((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))),vector_d(static_cast<Eigen::VectorXd::Index>(z_dim)));
-        for (int j1__ = 0U; j1__ < z_dim; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++i0__)
-                S_z[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,S_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable S_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda_z")))
-            throw std::runtime_error("variable one_over_lambda_z missing");
-        vals_r__ = context__.vals_r("one_over_lambda_z");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda_z", "logical_eq(prior_dist_z,6)", logical_eq(prior_dist_z,6));
-        context__.validate_dims("initialization", "one_over_lambda_z", "double", context__.to_vec(logical_eq(prior_dist_z,6)));
-        // generate_declaration one_over_lambda_z
-        std::vector<double> one_over_lambda_z(logical_eq(prior_dist_z,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist_z,6); ++i0__)
-            one_over_lambda_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist_z,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda_z: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),lp__));
-            else
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-        T__ aux_unscaled;
-        (void) aux_unscaled;  // dummy to suppress unused var warning
-        if (jacobian__)
-            aux_unscaled = in__.scalar_lb_constrain(0,lp__);
-        else
-            aux_unscaled = in__.scalar_lb_constrain(0);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_omega;
-        (void) z_omega;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ),lp__);
-        else
-            z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-
-        vector<T__> gamma_z;
-        size_t dim_gamma_z_0__ = has_intercept_z;
-        gamma_z.reserve(dim_gamma_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_z_0__; ++k_0__) {
-            if (jacobian__)
-                gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) ),lp__));
-            else
-                gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) )));
-        }
-
-        vector<T__> global_z;
-        size_t dim_global_z_0__ = hs_z;
-        global_z.reserve(dim_global_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_z_0__; ++k_0__) {
-            if (jacobian__)
-                global_z.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global_z.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local_z;
-        size_t dim_local_z_0__ = hs_z;
-        local_z.reserve(dim_local_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_z_0__; ++k_0__) {
-            if (jacobian__)
-                local_z.push_back(in__.vector_lb_constrain(0,z_dim,lp__));
-            else
-                local_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > S_z;
-        size_t dim_S_z_0__ = (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        S_z.reserve(dim_S_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_S_z_0__; ++k_0__) {
-            if (jacobian__)
-                S_z.push_back(in__.vector_lb_constrain(0,z_dim,lp__));
-            else
-                S_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-
-        vector<T__> one_over_lambda_z;
-        size_t dim_one_over_lambda_z_0__ = logical_eq(prior_dist_z,6);
-        one_over_lambda_z.reserve(dim_one_over_lambda_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_z_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda_z.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda_z.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<T__>(aux_unscaled) : stan::math::promote_scalar<T__>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<T__>(((prior_scale_for_aux * aux_unscaled) + prior_mean_for_aux)) : stan::math::promote_scalar<T__>((prior_scale_for_aux * aux_unscaled)) )) ));
-        validate_non_negative_index("omega", "z_dim", z_dim);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  omega(static_cast<Eigen::VectorXd::Index>(z_dim));
-        (void) omega;  // dummy to suppress unused var warning
-
-        stan::math::initialize(omega, DUMMY_VAR__);
-        stan::math::fill(omega,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_eq(prior_dist_z,0))) {
-                stan::math::assign(omega, z_omega);
-            } else if (as_bool(logical_eq(prior_dist_z,1))) {
-                stan::math::assign(omega, add(elt_multiply(z_omega,prior_scale_z),prior_mean_z));
-            } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                for (int k = 1; k <= z_dim; ++k) {
-
-                    stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((CFt(get_base1(omega,k,"omega",1),get_base1(prior_df_z,k,"prior_df_z",1), pstream__) * get_base1(prior_scale_z,k,"prior_scale_z",1)) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                stan::math::assign(omega, hs_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                stan::math::assign(omega, hsplus_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,5))) {
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(prior_scale_z,sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,6))) {
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),prior_scale_z),sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= z_dim; ++k) {
-
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), get_base1(z_omega,z_pos,"z_omega",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals_z,k,"num_normals_z",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(omega,k,"omega",1), (get_base1(omega,k,"omega",1) * get_base1(z_omega,z_pos,"z_omega",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((get_base1(omega,k,"omega",1) * pow(get_base1(prior_scale_z,k,"prior_scale_z",1),get_base1(num_normals_z,k,"num_normals_z",1))) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                    }
-                }
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, multiply(elt_multiply(scale,tau),aux));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < z_dim; ++i0__) {
-            if (stan::math::is_uninitialized(omega(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: omega" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta_z", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta_z(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta_z;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta_z, DUMMY_VAR__);
-                stan::math::fill(eta_z,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool((primitive_value((primitive_value(logical_eq(family,1)) || primitive_value(logical_eq(link,2)))) || primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_neq(link,5))))))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-                        stan::math::assign(eta, add(subtract(eta,max(eta)),get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        stan::math::assign(eta, add(subtract(eta,min(eta)),get_base1(gamma,1,"gamma",1)));
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                if (as_bool(logical_gt(SSfun,0))) {
-                    {
-                        validate_non_negative_index("P", "len_y", len_y);
-                        validate_non_negative_index("P", "K", K);
-                        Eigen::Matrix<T__,Eigen::Dynamic,Eigen::Dynamic>  P(static_cast<Eigen::VectorXd::Index>(len_y),static_cast<Eigen::VectorXd::Index>(K));
-                        (void) P;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(P, DUMMY_VAR__);
-                        stan::math::fill(P,DUMMY_VAR__);
-
-
-                        stan::math::assign(P, reshape_vec(eta,len_y,K, pstream__));
-                        if (as_bool(logical_lt(SSfun,5))) {
-
-                            if (as_bool(logical_lte(SSfun,2))) {
-
-                                if (as_bool(logical_eq(SSfun,1))) {
-                                    lp_accum__.add(normal_log(y,SS_asymp(input,P, pstream__),aux));
-                                } else {
-                                    lp_accum__.add(normal_log(y,SS_asympOff(input,P, pstream__),aux));
-                                }
-                            } else if (as_bool(logical_eq(SSfun,3))) {
-                                lp_accum__.add(normal_log(y,SS_asympOrig(input,P, pstream__),aux));
-                            } else {
-
-                                for (int i = 1; i <= len_y; ++i) {
-                                    stan::math::assign(get_base1_lhs(P,i,1,"P",1), (get_base1(P,i,1,"P",1) + exp(get_base1(P,i,3,"P",1))));
-                                }
-                                lp_accum__.add(normal_log(y,SS_biexp(input,P, pstream__),aux));
-                            }
-                        } else {
-
-                            if (as_bool(logical_lte(SSfun,7))) {
-
-                                if (as_bool(logical_eq(SSfun,5))) {
-                                    lp_accum__.add(normal_log(y,SS_fol(Dose,input,P, pstream__),aux));
-                                } else if (as_bool(logical_eq(SSfun,6))) {
-                                    lp_accum__.add(normal_log(y,SS_fpl(input,P, pstream__),aux));
-                                } else {
-                                    lp_accum__.add(normal_log(y,SS_gompertz(input,P, pstream__),aux));
-                                }
-                            } else {
-
-                                if (as_bool(logical_eq(SSfun,8))) {
-                                    lp_accum__.add(normal_log(y,SS_logis(input,P, pstream__),aux));
-                                } else if (as_bool(logical_eq(SSfun,9))) {
-                                    lp_accum__.add(normal_log(y,SS_micmen(input,P, pstream__),aux));
-                                } else {
-                                    lp_accum__.add(normal_log(y,SS_weibull(input,P, pstream__),aux));
-                                }
-                            }
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(z_dim,0)))) && primitive_value(logical_gt(link_phi,0))))) {
-
-                        stan::math::assign(eta_z, multiply(betareg_z,omega));
-                    } else if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(z_dim,0)))) && primitive_value(logical_eq(has_intercept_z,1))))) {
-
-                        stan::math::assign(eta_z, rep_vector(0.0,N));
-                    }
-                    if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                        if (as_bool(logical_gt(link_phi,1))) {
-
-                            stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),get_base1(gamma_z,1,"gamma_z",1)));
-                        } else {
-
-                            stan::math::assign(eta_z, add(eta_z,get_base1(gamma_z,1,"gamma_z",1)));
-                        }
-                    } else {
-
-                        if (as_bool(logical_gt(link_phi,1))) {
-
-                            stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),dot_product(zbar,omega)));
-                        } else {
-
-                            stan::math::assign(eta_z, add(eta_z,dot_product(zbar,omega)));
-                        }
-                    }
-                    if (as_bool(logical_eq(family,1))) {
-
-                        if (as_bool(logical_eq(link,1))) {
-                            lp_accum__.add(normal_log(y,eta,aux));
-                        } else if (as_bool(logical_eq(link,2))) {
-                            lp_accum__.add(normal_log(y,exp(eta),aux));
-                        } else {
-                            lp_accum__.add(normal_log(y,divide_real_by_vector(1,eta, pstream__),aux));
-                        }
-                    } else if (as_bool(logical_eq(family,2))) {
-
-                        lp_accum__.add(GammaReg(y,eta,aux,link,sum_log_y, pstream__));
-                    } else if (as_bool(logical_eq(family,3))) {
-
-                        lp_accum__.add(inv_gaussian(y,linkinv_inv_gaussian(eta,link, pstream__),aux,sum_log_y,sqrt_y, pstream__));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-                        {
-                            validate_non_negative_index("mu", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu, DUMMY_VAR__);
-                            stan::math::fill(mu,DUMMY_VAR__);
-
-
-                            stan::math::assign(mu, linkinv_beta(eta,link, pstream__));
-                            lp_accum__.add(beta_log(y,multiply(mu,aux),multiply(subtract(1,mu),aux)));
-                        }
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-                        {
-                            validate_non_negative_index("mu", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu, DUMMY_VAR__);
-                            stan::math::fill(mu,DUMMY_VAR__);
-                            validate_non_negative_index("mu_z", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu_z;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu_z, DUMMY_VAR__);
-                            stan::math::fill(mu_z,DUMMY_VAR__);
-
-
-                            stan::math::assign(mu, linkinv_beta(eta,link, pstream__));
-                            stan::math::assign(mu_z, linkinv_beta_z(eta_z,link_phi, pstream__));
-                            lp_accum__.add(beta_log(y,rows_dot_product(mu,mu_z),rows_dot_product(subtract(1,mu),mu_z)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    {
-                        validate_non_negative_index("summands", "N", N);
-                        Eigen::Matrix<T__,Eigen::Dynamic,1>  summands(static_cast<Eigen::VectorXd::Index>(N));
-                        (void) summands;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(summands, DUMMY_VAR__);
-                        stan::math::fill(summands,DUMMY_VAR__);
-
-
-                        if (as_bool(logical_eq(family,1))) {
-                            stan::math::assign(summands, pw_gauss(y,eta,aux,link, pstream__));
-                        } else if (as_bool(logical_eq(family,2))) {
-                            stan::math::assign(summands, pw_gamma(y,eta,aux,link, pstream__));
-                        } else if (as_bool(logical_eq(family,3))) {
-                            stan::math::assign(summands, pw_inv_gaussian(y,eta,aux,link,log_y,sqrt_y, pstream__));
-                        } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-                            stan::math::assign(summands, pw_beta(y,eta,aux,link, pstream__));
-                        } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-                            stan::math::assign(summands, pw_beta_z(y,eta,eta_z,link,link_phi, pstream__));
-                        }
-                        lp_accum__.add(dot_product(weights,summands));
-                    }
-                }
-                if (as_bool((primitive_value(logical_gt(prior_dist_for_aux,0)) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                            lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                        } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                            lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                        } else {
-                            lp_accum__.add(exponential_log(aux_unscaled,1));
-                        }
-                    }
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                if (as_bool(K_smooth)) {
-
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                if (as_bool(logical_eq(prior_dist_z,1))) {
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_omega,0,1));
-                        lp_accum__.add((normal_log(get_base1(local_z,1,"local_z",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,2,"local_z",1),multiply(0.5,prior_df_z),multiply(0.5,prior_df_z)));
-                        lp_accum__.add((normal_log(get_base1(global_z,1,"global_z",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global_z,2,"global_z",1),0.5,0.5));
-                    }
-                } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_omega,0,1));
-                        lp_accum__.add((normal_log(get_base1(local_z,1,"local_z",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,2,"local_z",1),multiply(0.5,prior_df_z),multiply(0.5,prior_df_z)));
-                        lp_accum__.add((normal_log(get_base1(local_z,3,"local_z",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,4,"local_z",1),multiply(0.5,prior_scale_z),multiply(0.5,prior_scale_z)));
-                        lp_accum__.add((normal_log(get_base1(global_z,1,"global_z",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global_z,2,"global_z",1),0.5,0.5));
-                    }
-                } else if (as_bool(logical_eq(prior_dist_z,5))) {
-
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                    lp_accum__.add(exponential_log(get_base1(S_z,1,"S_z",1),1));
-                } else if (as_bool(logical_eq(prior_dist_z,6))) {
-
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                    lp_accum__.add(exponential_log(get_base1(S_z,1,"S_z",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),get_base1(prior_df_z,1,"prior_df_z",1)));
-                } else if (as_bool(logical_eq(prior_dist_z,7))) {
-
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept_z,1))) {
-                        lp_accum__.add(normal_log(gamma_z,prior_mean_for_intercept_z,prior_scale_for_intercept_z));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept_z,2))) {
-                        lp_accum__.add(student_t_log(gamma_z,prior_df_for_intercept_z,prior_mean_for_intercept_z,prior_scale_for_intercept_z));
-                    }
-                }
-                if (as_bool(logical_gt(t,0))) {
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("aux_unscaled");
-        names__.push_back("z_omega");
-        names__.push_back("gamma_z");
-        names__.push_back("global_z");
-        names__.push_back("local_z");
-        names__.push_back("S_z");
-        names__.push_back("one_over_lambda_z");
-        names__.push_back("aux");
-        names__.push_back("omega");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("omega_int");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs_z);
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist_z,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_continuous_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        double aux_unscaled = in__.scalar_lb_constrain(0);
-        vector_d z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        vector<double> gamma_z;
-        size_t dim_gamma_z_0__ = has_intercept_z;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_z_0__; ++k_0__) {
-            gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) )));
-        }
-        vector<double> global_z;
-        size_t dim_global_z_0__ = hs_z;
-        for (size_t k_0__ = 0; k_0__ < dim_global_z_0__; ++k_0__) {
-            global_z.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local_z;
-        size_t dim_local_z_0__ = hs_z;
-        for (size_t k_0__ = 0; k_0__ < dim_local_z_0__; ++k_0__) {
-            local_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-        vector<vector_d> S_z;
-        size_t dim_S_z_0__ = (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_S_z_0__; ++k_0__) {
-            S_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-        vector<double> one_over_lambda_z;
-        size_t dim_one_over_lambda_z_0__ = logical_eq(prior_dist_z,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_z_0__; ++k_0__) {
-            one_over_lambda_z.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-        vars__.push_back(aux_unscaled);
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            vars__.push_back(z_omega[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept_z; ++k_0__) {
-            vars__.push_back(gamma_z[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs_z; ++k_0__) {
-            vars__.push_back(global_z[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < z_dim; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs_z; ++k_0__) {
-                vars__.push_back(local_z[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < z_dim; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                vars__.push_back(S_z[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist_z,6); ++k_0__) {
-            vars__.push_back(one_over_lambda_z[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<double>(aux_unscaled) : stan::math::promote_scalar<double>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<double>(((prior_scale_for_aux * aux_unscaled) + prior_mean_for_aux)) : stan::math::promote_scalar<double>((prior_scale_for_aux * aux_unscaled)) )) ));
-        validate_non_negative_index("omega", "z_dim", z_dim);
-        vector_d omega(static_cast<Eigen::VectorXd::Index>(z_dim));
-        (void) omega;  // dummy to suppress unused var warning
-
-        stan::math::initialize(omega, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(omega,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool(logical_eq(prior_dist_z,0))) {
-                stan::math::assign(omega, z_omega);
-            } else if (as_bool(logical_eq(prior_dist_z,1))) {
-                stan::math::assign(omega, add(elt_multiply(z_omega,prior_scale_z),prior_mean_z));
-            } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                for (int k = 1; k <= z_dim; ++k) {
-
-                    stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((CFt(get_base1(omega,k,"omega",1),get_base1(prior_df_z,k,"prior_df_z",1), pstream__) * get_base1(prior_scale_z,k,"prior_scale_z",1)) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                stan::math::assign(omega, hs_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                stan::math::assign(omega, hsplus_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,5))) {
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(prior_scale_z,sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,6))) {
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),prior_scale_z),sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= z_dim; ++k) {
-
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), get_base1(z_omega,z_pos,"z_omega",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals_z,k,"num_normals_z",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(omega,k,"omega",1), (get_base1(omega,k,"omega",1) * get_base1(z_omega,z_pos,"z_omega",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((get_base1(omega,k,"omega",1) * pow(get_base1(prior_scale_z,k,"prior_scale_z",1),get_base1(num_normals_z,k,"num_normals_z",1))) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                    }
-                }
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, multiply(elt_multiply(scale,tau),aux));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < z_dim; ++k_0__) {
-            vars__.push_back(omega[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("omega_int", "has_intercept_z", has_intercept_z);
-        vector<double> omega_int(has_intercept_z, 0.0);
-        stan::math::initialize(omega_int, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(omega_int,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                if (as_bool(dense_X)) {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                stan::math::assign(get_base1_lhs(omega_int,1,"omega_int",1), (get_base1(gamma_z,1,"gamma_z",1) - dot_product(zbar,omega)));
-            }
-            {
-                validate_non_negative_index("eta_z", "N", N);
-                vector_d eta_z(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta_z;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta_z, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta_z,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool((primitive_value(logical_eq(make_lower(family,link, pstream__),stan::math::negative_infinity())) && primitive_value(logical_eq(make_upper(family,link, pstream__),stan::math::positive_infinity()))))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-                        {
-                            double max_eta(0.0);
-                            (void) max_eta;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(max_eta, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(max_eta,DUMMY_VAR__);
-
-
-                            stan::math::assign(max_eta, max(eta));
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - max_eta));
-                            stan::math::assign(eta, add(subtract(eta,max_eta),get_base1(gamma,1,"gamma",1)));
-                        }
-                    } else {
-                        {
-                            double min_eta(0.0);
-                            (void) min_eta;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(min_eta, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(min_eta,DUMMY_VAR__);
-                            stan::math::assign(min_eta,min(eta));
-
-
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - min_eta));
-                            stan::math::assign(eta, add(subtract(eta,min_eta),get_base1(gamma,1,"gamma",1)));
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(z_dim,0)))) && primitive_value(logical_gt(link_phi,0))))) {
-
-                    stan::math::assign(eta_z, multiply(betareg_z,omega));
-                } else if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(z_dim,0)))) && primitive_value(logical_eq(has_intercept_z,1))))) {
-
-                    stan::math::assign(eta_z, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                    if (as_bool(logical_gt(link_phi,1))) {
-
-                        stan::math::assign(get_base1_lhs(omega_int,1,"omega_int",1), (get_base1(omega_int,1,"omega_int",1) - min(eta_z)));
-                        stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),get_base1(gamma_z,1,"gamma_z",1)));
-                    } else {
-
-                        stan::math::assign(eta_z, add(eta_z,get_base1(gamma_z,1,"gamma_z",1)));
-                    }
-                } else {
-
-                    if (as_bool(logical_gt(link_phi,1))) {
-
-                        stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),dot_product(zbar,omega)));
-                    } else {
-
-                        stan::math::assign(eta_z, add(eta_z,dot_product(zbar,omega)));
-                    }
-                }
-                if (as_bool(logical_gt(SSfun,0))) {
-                    {
-                        validate_non_negative_index("eta_nlmer", "len_y", len_y);
-                        vector_d eta_nlmer(static_cast<Eigen::VectorXd::Index>(len_y));
-                        (void) eta_nlmer;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(eta_nlmer, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(eta_nlmer,DUMMY_VAR__);
-                        validate_non_negative_index("P", "len_y", len_y);
-                        validate_non_negative_index("P", "K", K);
-                        matrix_d P(static_cast<Eigen::VectorXd::Index>(len_y),static_cast<Eigen::VectorXd::Index>(K));
-                        (void) P;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(P, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(P,DUMMY_VAR__);
-
-
-                        stan::math::assign(P, reshape_vec(eta,len_y,K, pstream__));
-                        if (as_bool(logical_lt(SSfun,5))) {
-
-                            if (as_bool(logical_lte(SSfun,2))) {
-
-                                if (as_bool(logical_eq(SSfun,1))) {
-                                    stan::math::assign(eta_nlmer, SS_asymp(input,P, pstream__));
-                                } else {
-                                    stan::math::assign(eta_nlmer, SS_asympOff(input,P, pstream__));
-                                }
-                            } else if (as_bool(logical_eq(SSfun,3))) {
-                                stan::math::assign(eta_nlmer, SS_asympOrig(input,P, pstream__));
-                            } else {
-                                stan::math::assign(eta_nlmer, SS_biexp(input,P, pstream__));
-                            }
-                        } else {
-
-                            if (as_bool(logical_lte(SSfun,7))) {
-
-                                if (as_bool(logical_eq(SSfun,5))) {
-                                    stan::math::assign(eta_nlmer, SS_fol(Dose,input,P, pstream__));
-                                } else if (as_bool(logical_eq(SSfun,6))) {
-                                    stan::math::assign(eta_nlmer, SS_fpl(input,P, pstream__));
-                                } else {
-                                    stan::math::assign(eta_nlmer, SS_gompertz(input,P, pstream__));
-                                }
-                            } else {
-
-                                if (as_bool(logical_eq(SSfun,8))) {
-                                    stan::math::assign(eta_nlmer, SS_logis(input,P, pstream__));
-                                } else if (as_bool(logical_eq(SSfun,9))) {
-                                    stan::math::assign(eta_nlmer, SS_micmen(input,P, pstream__));
-                                } else {
-                                    stan::math::assign(eta_nlmer, SS_weibull(input,P, pstream__));
-                                }
-                            }
-                        }
-                        for (int n = 1; n <= len_y; ++n) {
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta_nlmer,n,"eta_nlmer",1),aux, base_rng__)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,1))) {
-
-                    if (as_bool(logical_gt(link,1))) {
-                        stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    }
-                    for (int n = 1; n <= len_y; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),aux, base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    if (as_bool(logical_gt(link,1))) {
-                        stan::math::assign(eta, linkinv_gamma(eta,link, pstream__));
-                    }
-                    for (int n = 1; n <= len_y; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    if (as_bool(logical_gt(link,1))) {
-                        stan::math::assign(eta, linkinv_inv_gaussian(eta,link, pstream__));
-                    }
-                    for (int n = 1; n <= len_y; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + inv_gaussian_rng(get_base1(eta,n,"eta",1),aux, base_rng__, pstream__)));
-                    }
-                } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-
-                    stan::math::assign(eta, linkinv_beta(eta,link, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double eta_n(0.0);
-                            (void) eta_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(eta_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(eta_n,DUMMY_VAR__);
-                            stan::math::assign(eta_n,get_base1(eta,n,"eta",1));
-
-
-                            if (as_bool(logical_lte(aux,0))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(0.5, base_rng__)));
-                            } else if (as_bool(logical_gte(eta_n,1))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + 1));
-                            } else if (as_bool(logical_gt(eta_n,0))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + beta_rng((get_base1(eta,n,"eta",1) * aux),((1 - get_base1(eta,n,"eta",1)) * aux), base_rng__)));
-                            }
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-
-                    stan::math::assign(eta, linkinv_beta(eta,link, pstream__));
-                    stan::math::assign(eta_z, linkinv_beta_z(eta_z,link_phi, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double eta_n(0.0);
-                            (void) eta_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(eta_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(eta_n,DUMMY_VAR__);
-                            stan::math::assign(eta_n,get_base1(eta,n,"eta",1));
-                            double aux_n(0.0);
-                            (void) aux_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(aux_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(aux_n,DUMMY_VAR__);
-                            stan::math::assign(aux_n,get_base1(eta_z,n,"eta_z",1));
-
-
-                            if (as_bool(logical_lte(aux_n,0))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(0.5, base_rng__)));
-                            } else if (as_bool(logical_gte(eta_n,1))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + 1));
-                            } else if (as_bool(logical_gt(eta_n,0))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + beta_rng((eta_n * aux_n),((1 - eta_n) * aux_n), base_rng__)));
-                            }
-                        }
-                    }
-                }
-                stan::math::assign(mean_PPD, (mean_PPD / len_y));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept_z; ++k_0__) {
-            vars__.push_back(omega_int[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_continuous";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux_unscaled";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "S_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist_z,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= z_dim; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega_int" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux_unscaled";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "S_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist_z,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= z_dim; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega_int" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_count_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_count");
-    reader.add_event(735, 735, "end", "model_count");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    for (int c = 1; c <= r; ++c) {
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            for (int c = 1; c <= nc; ++c) {
-                                for (int r = c; r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            for (int c = 1; c <= nc; ++c) {
-
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    for (int s = 1; s <= nc; ++s) {
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            lp_accum__.add(normal_log(z_b,0,1));
-            lp_accum__.add(normal_log(z_T,0,1));
-            for (int i = 1; i <= t; ++i) {
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            stan::math::assign(nu, (nu - 0.5));
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            stan::math::assign(K, rows(z_beta));
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_gt(t,0))) {
-                for (int j = 1; j <= N; ++j) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_count(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_count_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_count(eta, link, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_pois(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,1))) {
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log_log(get_base1(y,n,"y",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,3))) {
-                {
-                    validate_non_negative_index("phi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) phi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(phi,DUMMY_VAR__);
-                    stan::math::assign(phi,linkinv_count(eta,link, pstream__));
-
-
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log(get_base1(y,n,"y",1),get_base1(phi,n,"phi",1)));
-                    }
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_pois_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_pois(y, eta, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-pw_nb(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("rho", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  rho(static_cast<Eigen::VectorXd::Index>(N));
-            (void) rho;  // dummy to suppress unused var warning
-
-            stan::math::initialize(rho, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(rho,DUMMY_VAR__);
-            stan::math::assign(rho,linkinv_count(eta,link, pstream__));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            for (int n = 1; n <= N; ++n) {
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), neg_binomial_2_log(get_base1(y,n,"y",1),get_base1(rho,n,"rho",1),theta));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_nb_functor__ {
-    template <typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) const {
-        return pw_nb(y, eta, theta, link, pstream__);
-    }
-};
-
-class model_count : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    vector<int> y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    double poisson_max;
-    vector<vector<int> > V;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_count(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_count(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_count_namespace::model_count";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        poisson_max = double(0);
-        stan::math::fill(poisson_max,DUMMY_VAR__);
-        stan::math::assign(poisson_max,pow(2.0,30.0));
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "N", N);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(N,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(N,(special_case ? t : 0 ),v, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            if (as_bool(logical_lte(prior_dist,2))) {
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                stan::math::assign(hs, 4);
-            } else {
-                stan::math::assign(hs, 0);
-            }
-            stan::math::assign(pos, 1);
-            for (int i = 1; i <= t; ++i) {
-
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < N; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-        validate_non_negative_index("aux_unscaled", "logical_gt(family,1)", logical_gt(family,1));
-        num_params_r__ += logical_gt(family,1);
-        validate_non_negative_index("noise", "N", N);
-        validate_non_negative_index("noise", "logical_eq(family,3)", logical_eq(family,3));
-        num_params_r__ += N * logical_eq(family,3);
-    }
-
-    ~model_count() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        validate_non_negative_index("aux_unscaled", "logical_gt(family,1)", logical_gt(family,1));
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec(logical_gt(family,1)));
-        // generate_declaration aux_unscaled
-        std::vector<double> aux_unscaled(logical_gt(family,1),double(0));
-        for (int i0__ = 0U; i0__ < logical_gt(family,1); ++i0__)
-            aux_unscaled[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_gt(family,1); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("noise")))
-            throw std::runtime_error("variable noise missing");
-        vals_r__ = context__.vals_r("noise");
-        pos__ = 0U;
-        validate_non_negative_index("noise", "logical_eq(family,3)", logical_eq(family,3));
-        validate_non_negative_index("noise", "N", N);
-        context__.validate_dims("initialization", "noise", "vector_d", context__.to_vec(logical_eq(family,3),N));
-        // generate_declaration noise
-        std::vector<vector_d> noise(logical_eq(family,3),vector_d(static_cast<Eigen::VectorXd::Index>(N)));
-        for (int j1__ = 0U; j1__ < N; ++j1__)
-            for (int i0__ = 0U; i0__ < logical_eq(family,3); ++i0__)
-                noise[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(family,3); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,noise[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable noise: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 ),lp__));
-            else
-                gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-        vector<T__> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = logical_gt(family,1);
-        aux_unscaled.reserve(dim_aux_unscaled_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            if (jacobian__)
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > noise;
-        size_t dim_noise_0__ = logical_eq(family,3);
-        noise.reserve(dim_noise_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_noise_0__; ++k_0__) {
-            if (jacobian__)
-                noise.push_back(in__.vector_lb_constrain(0,N,lp__));
-            else
-                noise.push_back(in__.vector_lb_constrain(0,N));
-        }
-
-
-        // transformed parameters
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::negative_infinity());
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool((primitive_value(logical_gt(family,1)) && primitive_value((primitive_value(logical_eq(prior_dist_for_aux,0)) || primitive_value(logical_lte(prior_scale_for_aux,0))))))) {
-                stan::math::assign(aux, get_base1(aux_unscaled,1,"aux_unscaled",1));
-            } else if (as_bool(logical_gt(family,1))) {
-
-                stan::math::assign(aux, (prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)));
-                if (as_bool(logical_lte(prior_dist_for_aux,2))) {
-                    stan::math::assign(aux, (aux + prior_mean_for_aux));
-                }
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,(logical_eq(family,1) ? stan::math::promote_scalar<T__>(tau) : stan::math::promote_scalar<T__>(multiply(tau,aux)) )));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    if (as_bool(logical_eq(family,1))) {
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    } else {
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    }
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(link,1))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        stan::math::assign(eta, add(subtract(eta,min(eta)),get_base1(gamma,1,"gamma",1)));
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                if (as_bool(logical_eq(family,3))) {
-
-                    if (as_bool(logical_eq(link,1))) {
-                        stan::math::assign(eta, add(add(eta,log(aux)),log(get_base1(noise,1,"noise",1))));
-                    } else if (as_bool(logical_eq(link,2))) {
-                        stan::math::assign(eta, elt_multiply(multiply(eta,aux),get_base1(noise,1,"noise",1)));
-                    } else {
-                        stan::math::assign(eta, add(add(eta,sqrt(aux)),sqrt(get_base1(noise,1,"noise",1))));
-                    }
-                }
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    if (as_bool(logical_neq(family,2))) {
-
-                        if (as_bool(logical_eq(link,1))) {
-                            lp_accum__.add(poisson_log_log(y,eta));
-                        } else {
-                            lp_accum__.add(poisson_log(y,linkinv_count(eta,link, pstream__)));
-                        }
-                    } else {
-
-                        if (as_bool(logical_eq(link,1))) {
-                            lp_accum__.add(neg_binomial_2_log_log(y,eta,aux));
-                        } else {
-                            lp_accum__.add(neg_binomial_2_log(y,linkinv_count(eta,link, pstream__),aux));
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_neq(family,2)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    lp_accum__.add(dot_product(weights,pw_pois(y,eta,link, pstream__)));
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    lp_accum__.add(dot_product(weights,pw_nb(y,eta,aux,link, pstream__)));
-                }
-                if (as_bool((primitive_value((primitive_value(logical_gt(family,1)) && primitive_value(logical_gt(prior_dist_for_aux,0)))) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                            lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                        } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                            lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                        } else {
-                            lp_accum__.add(exponential_log(aux_unscaled,1));
-                        }
-                    }
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                if (as_bool(K_smooth)) {
-
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                if (as_bool(logical_eq(family,3))) {
-                    lp_accum__.add(gamma_log(get_base1(noise,1,"noise",1),aux,1));
-                }
-                if (as_bool(logical_gt(t,0))) {
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("aux_unscaled");
-        names__.push_back("noise");
-        names__.push_back("aux");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_gt(family,1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(family,3));
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_count_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        vector<double> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = logical_gt(family,1);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> noise;
-        size_t dim_noise_0__ = logical_eq(family,3);
-        for (size_t k_0__ = 0; k_0__ < dim_noise_0__; ++k_0__) {
-            noise.push_back(in__.vector_lb_constrain(0,N));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_gt(family,1); ++k_0__) {
-            vars__.push_back(aux_unscaled[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < N; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < logical_eq(family,3); ++k_0__) {
-                vars__.push_back(noise[k_0__][k_1__]);
-            }
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::negative_infinity());
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            if (as_bool(K_smooth)) {
-
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            if (as_bool((primitive_value(logical_gt(family,1)) && primitive_value((primitive_value(logical_eq(prior_dist_for_aux,0)) || primitive_value(logical_lte(prior_scale_for_aux,0))))))) {
-                stan::math::assign(aux, get_base1(aux_unscaled,1,"aux_unscaled",1));
-            } else if (as_bool(logical_gt(family,1))) {
-
-                stan::math::assign(aux, (prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)));
-                if (as_bool(logical_lte(prior_dist_for_aux,2))) {
-                    stan::math::assign(aux, (aux + prior_mean_for_aux));
-                }
-            }
-            if (as_bool(logical_gt(t,0))) {
-
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        stan::math::assign(theta_L, elt_multiply(scale,(logical_eq(family,1) ? stan::math::promote_scalar<double>(tau) : stan::math::promote_scalar<double>(multiply(tau,aux)) )));
-                        if (as_bool(logical_eq(t,1))) {
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    if (as_bool(logical_eq(family,1))) {
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    } else {
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    }
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                if (as_bool(dense_X)) {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("nu", "N", N);
-                vector_d nu(static_cast<Eigen::VectorXd::Index>(N));
-                (void) nu;  // dummy to suppress unused var warning
-
-                stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(nu,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool(logical_gt(t,0))) {
-
-                    if (as_bool(special_case)) {
-                        for (int i = 1; i <= t; ++i) {
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(link,1))) {
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            stan::math::assign(shift, min(eta));
-                            stan::math::assign(eta, add(subtract(eta,shift),get_base1(gamma,1,"gamma",1)));
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                } else {
-
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                if (as_bool(logical_eq(family,3))) {
-
-                    if (as_bool(logical_eq(link,1))) {
-                        stan::math::assign(eta, add(add(eta,log(aux)),log(get_base1(noise,1,"noise",1))));
-                    } else if (as_bool(logical_eq(link,2))) {
-                        stan::math::assign(eta, elt_multiply(multiply(eta,aux),get_base1(noise,1,"noise",1)));
-                    } else {
-                        stan::math::assign(eta, add(add(eta,sqrt(aux)),sqrt(get_base1(noise,1,"noise",1))));
-                    }
-                }
-                stan::math::assign(nu, linkinv_count(eta,link, pstream__));
-                if (as_bool(logical_neq(family,2))) {
-                    for (int n = 1; n <= N; ++n) {
-
-                        if (as_bool(logical_lt(get_base1(nu,n,"nu",1),poisson_max))) {
-                            stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(get_base1(nu,n,"nu",1), base_rng__)));
-                        } else {
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(nu,n,"nu",1),sqrt(get_base1(nu,n,"nu",1)), base_rng__)));
-                        }
-                    }
-                } else {
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double gamma_temp(0.0);
-                            (void) gamma_temp;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(gamma_temp, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(gamma_temp,DUMMY_VAR__);
-
-
-                            if (as_bool(is_inf(aux))) {
-                                stan::math::assign(gamma_temp, get_base1(nu,n,"nu",1));
-                            } else {
-                                stan::math::assign(gamma_temp, gamma_rng(aux,(aux / get_base1(nu,n,"nu",1)), base_rng__));
-                            }
-                            if (as_bool(logical_lt(gamma_temp,poisson_max))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(gamma_temp, base_rng__)));
-                            } else {
-                                stan::math::assign(mean_PPD, (mean_PPD + normal_rng(gamma_temp,sqrt(gamma_temp), base_rng__)));
-                            }
-                        }
-                    }
-                }
-                stan::math::assign(mean_PPD, (mean_PPD / N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_count";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_gt(family,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= N; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= logical_eq(family,3); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "noise" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_gt(family,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= N; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= logical_eq(family,3); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "noise" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_lm_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_lm");
-    reader.add_event(133, 133, "end", "model_lm");
-    return reader;
-}
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type
-ll_mvn_ols_qr_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& theta,
-                     const Eigen::Matrix<T1__, Eigen::Dynamic,1>& b,
-                     const T2__& intercept,
-                     const T3__& ybar,
-                     const T4__& SSR,
-                     const T5__& sigma,
-                     const int& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        lp_accum__.add((((-(0.5) * ((dot_self(subtract(theta,b)) + (N * square((intercept - ybar)))) + SSR)) / square(sigma)) - (N * (log(sigma) + 0.91893853320467267))));
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_mvn_ols_qr_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& theta,
-                     const Eigen::Matrix<T1__, Eigen::Dynamic,1>& b,
-                     const T2__& intercept,
-                     const T3__& ybar,
-                     const T4__& SSR,
-                     const T5__& sigma,
-                     const int& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_mvn_ols_qr_lp(theta, b, intercept, ybar, SSR, sigma, N, lp__, lp_accum__, pstream__);
-    }
-};
-
-class model_lm : public prob_grad {
-private:
-    int has_intercept;
-    int prior_dist_for_intercept;
-    double prior_scale_for_intercept;
-    double prior_mean_for_intercept;
-    int prior_dist;
-    int prior_PD;
-    double eta;
-    int J;
-    vector<int> N;
-    int K;
-    vector<vector_d> xbarR_inv;
-    vector<double> ybar;
-    double center_y;
-    vector<double> s_Y;
-    vector<vector_d> Rb;
-    vector<double> SSR;
-    vector<matrix_d> R_inv;
-    double half_K;
-    vector<double> sqrt_inv_N;
-    vector<double> sqrt_Nm1;
-public:
-    model_lm(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_lm(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_lm_namespace::model_lm";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "eta", "double", context__.to_vec());
-        eta = double(0);
-        vals_r__ = context__.vals_r("eta");
-        pos__ = 0;
-        eta = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "J", "int", context__.to_vec());
-        J = int(0);
-        vals_i__ = context__.vals_i("J");
-        pos__ = 0;
-        J = vals_i__[pos__++];
-        validate_non_negative_index("N", "J", J);
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec(J));
-        validate_non_negative_index("N", "J", J);
-        N = std::vector<int>(J,int(0));
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        size_t N_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < N_limit_0__; ++i_0__) {
-            N[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbarR_inv", "J", J);
-        validate_non_negative_index("xbarR_inv", "K", K);
-        context__.validate_dims("data initialization", "xbarR_inv", "vector_d", context__.to_vec(J,K));
-        validate_non_negative_index("xbarR_inv", "J", J);
-        validate_non_negative_index("xbarR_inv", "K", K);
-        xbarR_inv = std::vector<vector_d>(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("xbarR_inv");
-        pos__ = 0;
-        size_t xbarR_inv_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbarR_inv_i_vec_lim__; ++i_vec__) {
-            size_t xbarR_inv_limit_0__ = J;
-            for (size_t i_0__ = 0; i_0__ < xbarR_inv_limit_0__; ++i_0__) {
-                xbarR_inv[i_0__][i_vec__] = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("ybar", "J", J);
-        context__.validate_dims("data initialization", "ybar", "double", context__.to_vec(J));
-        validate_non_negative_index("ybar", "J", J);
-        ybar = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("ybar");
-        pos__ = 0;
-        size_t ybar_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < ybar_limit_0__; ++i_0__) {
-            ybar[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "center_y", "double", context__.to_vec());
-        center_y = double(0);
-        vals_r__ = context__.vals_r("center_y");
-        pos__ = 0;
-        center_y = vals_r__[pos__++];
-        validate_non_negative_index("s_Y", "J", J);
-        context__.validate_dims("data initialization", "s_Y", "double", context__.to_vec(J));
-        validate_non_negative_index("s_Y", "J", J);
-        s_Y = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("s_Y");
-        pos__ = 0;
-        size_t s_Y_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < s_Y_limit_0__; ++i_0__) {
-            s_Y[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("Rb", "J", J);
-        validate_non_negative_index("Rb", "K", K);
-        context__.validate_dims("data initialization", "Rb", "vector_d", context__.to_vec(J,K));
-        validate_non_negative_index("Rb", "J", J);
-        validate_non_negative_index("Rb", "K", K);
-        Rb = std::vector<vector_d>(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("Rb");
-        pos__ = 0;
-        size_t Rb_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < Rb_i_vec_lim__; ++i_vec__) {
-            size_t Rb_limit_0__ = J;
-            for (size_t i_0__ = 0; i_0__ < Rb_limit_0__; ++i_0__) {
-                Rb[i_0__][i_vec__] = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("SSR", "J", J);
-        context__.validate_dims("data initialization", "SSR", "double", context__.to_vec(J));
-        validate_non_negative_index("SSR", "J", J);
-        SSR = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("SSR");
-        pos__ = 0;
-        size_t SSR_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < SSR_limit_0__; ++i_0__) {
-            SSR[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("R_inv", "J", J);
-        validate_non_negative_index("R_inv", "K", K);
-        validate_non_negative_index("R_inv", "K", K);
-        context__.validate_dims("data initialization", "R_inv", "matrix_d", context__.to_vec(J,K,K));
-        validate_non_negative_index("R_inv", "J", J);
-        validate_non_negative_index("R_inv", "K", K);
-        validate_non_negative_index("R_inv", "K", K);
-        R_inv = std::vector<matrix_d>(J,matrix_d(static_cast<Eigen::VectorXd::Index>(K),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("R_inv");
-        pos__ = 0;
-        size_t R_inv_m_mat_lim__ = K;
-        size_t R_inv_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < R_inv_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < R_inv_m_mat_lim__; ++m_mat__) {
-                size_t R_inv_limit_0__ = J;
-                for (size_t i_0__ = 0; i_0__ < R_inv_limit_0__; ++i_0__) {
-                    R_inv[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-
-        // validate, data variables
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,1);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,1);
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"eta",eta,0);
-        check_greater_or_equal(function__,"J",J,1);
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"N[k0__]",N[k0__],1);
-        }
-        check_greater_or_equal(function__,"K",K,1);
-        check_less_or_equal(function__,"K",K,min(N));
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"s_Y[k0__]",s_Y[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"SSR[k0__]",SSR[k0__],0);
-        }
-        // initialize data variables
-        half_K = double(0);
-        stan::math::fill(half_K,DUMMY_VAR__);
-        stan::math::assign(half_K,(0.5 * K));
-        validate_non_negative_index("sqrt_inv_N", "J", J);
-        sqrt_inv_N = std::vector<double>(J,double(0));
-        stan::math::fill(sqrt_inv_N,DUMMY_VAR__);
-        validate_non_negative_index("sqrt_Nm1", "J", J);
-        sqrt_Nm1 = std::vector<double>(J,double(0));
-        stan::math::fill(sqrt_Nm1,DUMMY_VAR__);
-
-        try {
-            for (int j = 1; j <= J; ++j) {
-
-                stan::math::assign(get_base1_lhs(sqrt_inv_N,j,"sqrt_inv_N",1), sqrt((1.0 / get_base1(N,j,"N",1))));
-                stan::math::assign(get_base1_lhs(sqrt_Nm1,j,"sqrt_Nm1",1), sqrt((get_base1(N,j,"N",1) - 1.0)));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("u", "K", K);
-        validate_non_negative_index("u", "J", J);
-        num_params_r__ += (K) * J;
-        validate_non_negative_index("z_alpha", "(J * has_intercept)", (J * has_intercept));
-        num_params_r__ += (J * has_intercept);
-        validate_non_negative_index("R2", "J", J);
-        num_params_r__ += J;
-        validate_non_negative_index("log_omega", "(J * (1 - prior_PD))", (J * (1 - prior_PD)));
-        num_params_r__ += (J * (1 - prior_PD));
-    }
-
-    ~model_lm() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("u")))
-            throw std::runtime_error("variable u missing");
-        vals_r__ = context__.vals_r("u");
-        pos__ = 0U;
-        validate_non_negative_index("u", "J", J);
-        validate_non_negative_index("u", "K", K);
-        context__.validate_dims("initialization", "u", "vector_d", context__.to_vec(J,K));
-        // generate_declaration u
-        std::vector<vector_d> u(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < J; ++i0__)
-                u[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            try {
-            writer__.unit_vector_unconstrain(u[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable u: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_alpha")))
-            throw std::runtime_error("variable z_alpha missing");
-        vals_r__ = context__.vals_r("z_alpha");
-        pos__ = 0U;
-        validate_non_negative_index("z_alpha", "(J * has_intercept)", (J * has_intercept));
-        context__.validate_dims("initialization", "z_alpha", "double", context__.to_vec((J * has_intercept)));
-        // generate_declaration z_alpha
-        std::vector<double> z_alpha((J * has_intercept),double(0));
-        for (int i0__ = 0U; i0__ < (J * has_intercept); ++i0__)
-            z_alpha[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (J * has_intercept); ++i0__)
-            try {
-            writer__.scalar_unconstrain(z_alpha[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_alpha: ") + e.what());
-        }
-
-        if (!(context__.contains_r("R2")))
-            throw std::runtime_error("variable R2 missing");
-        vals_r__ = context__.vals_r("R2");
-        pos__ = 0U;
-        validate_non_negative_index("R2", "J", J);
-        context__.validate_dims("initialization", "R2", "double", context__.to_vec(J));
-        // generate_declaration R2
-        std::vector<double> R2(J,double(0));
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            R2[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(0,1,R2[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable R2: ") + e.what());
-        }
-
-        if (!(context__.contains_r("log_omega")))
-            throw std::runtime_error("variable log_omega missing");
-        vals_r__ = context__.vals_r("log_omega");
-        pos__ = 0U;
-        validate_non_negative_index("log_omega", "(J * (1 - prior_PD))", (J * (1 - prior_PD)));
-        context__.validate_dims("initialization", "log_omega", "vector_d", context__.to_vec((J * (1 - prior_PD))));
-        // generate_declaration log_omega
-        vector_d log_omega(static_cast<Eigen::VectorXd::Index>((J * (1 - prior_PD))));
-        for (int j1__ = 0U; j1__ < (J * (1 - prior_PD)); ++j1__)
-            log_omega(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(log_omega);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable log_omega: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > u;
-        size_t dim_u_0__ = J;
-        u.reserve(dim_u_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_u_0__; ++k_0__) {
-            if (jacobian__)
-                u.push_back(in__.unit_vector_constrain(K,lp__));
-            else
-                u.push_back(in__.unit_vector_constrain(K));
-        }
-
-        vector<T__> z_alpha;
-        size_t dim_z_alpha_0__ = (J * has_intercept);
-        z_alpha.reserve(dim_z_alpha_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_z_alpha_0__; ++k_0__) {
-            if (jacobian__)
-                z_alpha.push_back(in__.scalar_constrain(lp__));
-            else
-                z_alpha.push_back(in__.scalar_constrain());
-        }
-
-        vector<T__> R2;
-        size_t dim_R2_0__ = J;
-        R2.reserve(dim_R2_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_R2_0__; ++k_0__) {
-            if (jacobian__)
-                R2.push_back(in__.scalar_lub_constrain(0,1,lp__));
-            else
-                R2.push_back(in__.scalar_lub_constrain(0,1));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  log_omega;
-        (void) log_omega;  // dummy to suppress unused var warning
-        if (jacobian__)
-            log_omega = in__.vector_constrain((J * (1 - prior_PD)),lp__);
-        else
-            log_omega = in__.vector_constrain((J * (1 - prior_PD)));
-
-
-        // transformed parameters
-        validate_non_negative_index("alpha", "(J * has_intercept)", (J * has_intercept));
-        vector<T__> alpha((J * has_intercept));
-        stan::math::initialize(alpha, DUMMY_VAR__);
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("theta", "K", K);
-        validate_non_negative_index("theta", "J", J);
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > theta(J, (Eigen::Matrix<T__,Eigen::Dynamic,1> (static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(theta, DUMMY_VAR__);
-        stan::math::fill(theta,DUMMY_VAR__);
-        validate_non_negative_index("sigma", "J", J);
-        vector<T__> sigma(J);
-        stan::math::initialize(sigma, DUMMY_VAR__);
-        stan::math::fill(sigma,DUMMY_VAR__);
-
-
-        try {
-            for (int j = 1; j <= J; ++j) {
-                {
-                    T__ Delta_y;
-                    (void) Delta_y;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Delta_y, DUMMY_VAR__);
-                    stan::math::fill(Delta_y,DUMMY_VAR__);
-                    stan::math::assign(Delta_y,(logical_eq(prior_PD,0) ? stan::math::promote_scalar<T__>((get_base1(s_Y,j,"s_Y",1) * exp(get_base1(log_omega,j,"log_omega",1)))) : stan::math::promote_scalar<T__>(1) ));
-
-
-                    if (as_bool(logical_gt(K,1))) {
-                        stan::math::assign(get_base1_lhs(theta,j,"theta",1), multiply(multiply(multiply(get_base1(u,j,"u",1),sqrt(get_base1(R2,j,"R2",1))),get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)),Delta_y));
-                    } else {
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(theta,j,"theta",1),1,"theta",2), (((get_base1(get_base1(u,j,"u",1),1,"u",2) * sqrt(get_base1(R2,j,"R2",1))) * get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)) * Delta_y));
-                    }
-                    stan::math::assign(get_base1_lhs(sigma,j,"sigma",1), (Delta_y * sqrt((1 - get_base1(R2,j,"R2",1)))));
-                    if (as_bool(logical_eq(has_intercept,1))) {
-
-                        if (as_bool(logical_eq(prior_dist_for_intercept,0))) {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), get_base1(z_alpha,j,"z_alpha",1));
-                        } else if (as_bool(logical_eq(prior_scale_for_intercept,0))) {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), (((get_base1(z_alpha,j,"z_alpha",1) * Delta_y) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)) + prior_mean_for_intercept));
-                        } else {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), ((get_base1(z_alpha,j,"z_alpha",1) * prior_scale_for_intercept) + prior_mean_for_intercept));
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < (J * has_intercept); ++i0__) {
-            if (stan::math::is_uninitialized(alpha[i0__])) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: alpha" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < J; ++i0__) {
-            for (int i1__ = 0; i1__ < K; ++i1__) {
-                if (stan::math::is_uninitialized(theta[i0__](i1__))) {
-                    std::stringstream msg__;
-                    msg__ << "Undefined transformed parameter: theta" << '[' << i0__ << ']' << '[' << i1__ << ']';
-                    throw std::runtime_error(msg__.str());
-                }
-            }
-        }
-        for (int i0__ = 0; i0__ < J; ++i0__) {
-            if (stan::math::is_uninitialized(sigma[i0__])) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: sigma" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"sigma[k0__]",sigma[k0__],0);
-        }
-
-        // model body
-        try {
-
-            for (int j = 1; j <= J; ++j) {
-
-                if (as_bool(logical_eq(prior_PD,0))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-                        T__ shift;
-                        (void) shift;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shift, DUMMY_VAR__);
-                        stan::math::fill(shift,DUMMY_VAR__);
-
-
-                        stan::math::assign(shift, dot_product(get_base1(xbarR_inv,j,"xbarR_inv",1),get_base1(theta,j,"theta",1)));
-                        stan::math::assign(dummy, ll_mvn_ols_qr_lp(get_base1(theta,j,"theta",1),get_base1(Rb,j,"Rb",1),(logical_eq(has_intercept,1) ? stan::math::promote_scalar<T__>((get_base1(alpha,j,"alpha",1) + shift)) : stan::math::promote_scalar<T__>(shift) ),get_base1(ybar,j,"ybar",1),get_base1(SSR,j,"SSR",1),get_base1(sigma,j,"sigma",1),get_base1(N,j,"N",1), lp__, lp_accum__, pstream__));
-                    }
-                }
-            }
-            if (as_bool((primitive_value(logical_eq(has_intercept,1)) && primitive_value(logical_gt(prior_dist_for_intercept,0))))) {
-                lp_accum__.add(normal_log(z_alpha,0,1));
-            }
-            if (as_bool(logical_eq(prior_dist,1))) {
-                lp_accum__.add(beta_log(R2,half_K,eta));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("u");
-        names__.push_back("z_alpha");
-        names__.push_back("R2");
-        names__.push_back("log_omega");
-        names__.push_back("alpha");
-        names__.push_back("theta");
-        names__.push_back("sigma");
-        names__.push_back("mean_PPD");
-        names__.push_back("beta");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * has_intercept));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * (1 - prior_PD)));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * has_intercept));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_lm_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<vector_d> u;
-        size_t dim_u_0__ = J;
-        for (size_t k_0__ = 0; k_0__ < dim_u_0__; ++k_0__) {
-            u.push_back(in__.unit_vector_constrain(K));
-        }
-        vector<double> z_alpha;
-        size_t dim_z_alpha_0__ = (J * has_intercept);
-        for (size_t k_0__ = 0; k_0__ < dim_z_alpha_0__; ++k_0__) {
-            z_alpha.push_back(in__.scalar_constrain());
-        }
-        vector<double> R2;
-        size_t dim_R2_0__ = J;
-        for (size_t k_0__ = 0; k_0__ < dim_R2_0__; ++k_0__) {
-            R2.push_back(in__.scalar_lub_constrain(0,1));
-        }
-        vector_d log_omega = in__.vector_constrain((J * (1 - prior_PD)));
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(u[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < (J * has_intercept); ++k_0__) {
-            vars__.push_back(z_alpha[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(R2[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J * (1 - prior_PD)); ++k_0__) {
-            vars__.push_back(log_omega[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("alpha", "(J * has_intercept)", (J * has_intercept));
-        vector<double> alpha((J * has_intercept), 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("theta", "K", K);
-        validate_non_negative_index("theta", "J", J);
-        vector<vector_d> theta(J, (vector_d(static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(theta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta,DUMMY_VAR__);
-        validate_non_negative_index("sigma", "J", J);
-        vector<double> sigma(J, 0.0);
-        stan::math::initialize(sigma, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(sigma,DUMMY_VAR__);
-
-
-        try {
-            for (int j = 1; j <= J; ++j) {
-                {
-                    double Delta_y(0.0);
-                    (void) Delta_y;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Delta_y, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Delta_y,DUMMY_VAR__);
-                    stan::math::assign(Delta_y,(logical_eq(prior_PD,0) ? stan::math::promote_scalar<double>((get_base1(s_Y,j,"s_Y",1) * exp(get_base1(log_omega,j,"log_omega",1)))) : stan::math::promote_scalar<double>(1) ));
-
-
-                    if (as_bool(logical_gt(K,1))) {
-                        stan::math::assign(get_base1_lhs(theta,j,"theta",1), multiply(multiply(multiply(get_base1(u,j,"u",1),sqrt(get_base1(R2,j,"R2",1))),get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)),Delta_y));
-                    } else {
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(theta,j,"theta",1),1,"theta",2), (((get_base1(get_base1(u,j,"u",1),1,"u",2) * sqrt(get_base1(R2,j,"R2",1))) * get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)) * Delta_y));
-                    }
-                    stan::math::assign(get_base1_lhs(sigma,j,"sigma",1), (Delta_y * sqrt((1 - get_base1(R2,j,"R2",1)))));
-                    if (as_bool(logical_eq(has_intercept,1))) {
-
-                        if (as_bool(logical_eq(prior_dist_for_intercept,0))) {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), get_base1(z_alpha,j,"z_alpha",1));
-                        } else if (as_bool(logical_eq(prior_scale_for_intercept,0))) {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), (((get_base1(z_alpha,j,"z_alpha",1) * Delta_y) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)) + prior_mean_for_intercept));
-                        } else {
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), ((get_base1(z_alpha,j,"z_alpha",1) * prior_scale_for_intercept) + prior_mean_for_intercept));
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"sigma[k0__]",sigma[k0__],0);
-        }
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < (J * has_intercept); ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(theta[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(sigma[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("mean_PPD", "J", J);
-        vector<double> mean_PPD(J, 0.0);
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        validate_non_negative_index("beta", "J", J);
-        vector<vector_d> beta(J, (vector_d(static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-
-
-        try {
-            for (int j = 1; j <= J; ++j) {
-                {
-                    double shift(0.0);
-                    (void) shift;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(shift,DUMMY_VAR__);
-
-
-                    stan::math::assign(shift, dot_product(get_base1(xbarR_inv,j,"xbarR_inv",1),get_base1(theta,j,"theta",1)));
-                    stan::math::assign(get_base1_lhs(mean_PPD,j,"mean_PPD",1), normal_rng((logical_eq(has_intercept,1) ? stan::math::promote_scalar<double>((get_base1(alpha,j,"alpha",1) + shift)) : stan::math::promote_scalar<double>(shift) ),(get_base1(sigma,j,"sigma",1) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)), base_rng__));
-                    stan::math::assign(get_base1_lhs(beta,j,"beta",1), multiply(get_base1(R_inv,j,"R_inv",1),get_base1(theta,j,"theta",1)));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(mean_PPD[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(beta[k_0__][k_1__]);
-            }
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_lm";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "u" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "R2" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J * (1 - prior_PD)); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "log_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "theta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "sigma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "beta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "u" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "R2" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J * (1 - prior_PD)); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "log_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "theta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "sigma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "beta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_polr_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_polr");
-    reader.add_event(313, 313, "end", "model_polr");
-    return reader;
-}
-
-template <typename T0__>
-typename boost::math::tools::promote_args<T0__>::type
-CDF_polr(const T0__& x,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(x));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(x));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(gumbel_cdf(x,0,1));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(x));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(cauchy_cdf(x,0,1));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CDF_polr_functor__ {
-    template <typename T0__>
-        typename boost::math::tools::promote_args<T0__>::type
-    operator()(const T0__& x,
-             const int& link, std::ostream* pstream__) const {
-        return CDF_polr(x, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__, T4__>::type, Eigen::Dynamic,1>
-pw_polr(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const Eigen::Matrix<T2__, Eigen::Dynamic,1>& cutpoints,
-            const int& link,
-            const T4__& alpha, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            int J(0);
-            (void) J;  // dummy to suppress unused var warning
-
-            stan::math::fill(J, std::numeric_limits<int>::min());
-            stan::math::assign(J,(rows(cutpoints) + 1));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool((primitive_value(logical_lt(link,1)) || primitive_value(logical_gt(link,5))))) {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            if (as_bool(logical_eq(alpha,1))) {
-                for (int n = 1; n <= N; ++n) {
-
-                    if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                    } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (1 - CDF_polr((get_base1(cutpoints,(J - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__)));
-                    } else {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (CDF_polr((get_base1(cutpoints,get_base1(y,n,"y",1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__) - CDF_polr((get_base1(cutpoints,(get_base1(y,n,"y",1) - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__)));
-                    }
-                }
-            } else {
-                for (int n = 1; n <= N; ++n) {
-
-                    if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), pow(CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__),alpha));
-                    } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (1 - pow(CDF_polr((get_base1(cutpoints,(J - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__),alpha)));
-                    } else {
-                        std::stringstream errmsg_stream__;
-                        errmsg_stream__ << "alpha not allowed with more than 2 outcome categories";
-                        throw std::domain_error(errmsg_stream__.str());
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(log(ll));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_polr_functor__ {
-    template <typename T1__, typename T2__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__, T4__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const Eigen::Matrix<T2__, Eigen::Dynamic,1>& cutpoints,
-            const int& link,
-            const T4__& alpha, std::ostream* pstream__) const {
-        return pw_polr(y, eta, cutpoints, link, alpha, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_cutpoints(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& probabilities,
-                   const T1__& scale,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int C(0);
-            (void) C;  // dummy to suppress unused var warning
-
-            stan::math::fill(C, std::numeric_limits<int>::min());
-            stan::math::assign(C,(rows(probabilities) - 1));
-            validate_non_negative_index("cutpoints", "C", C);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  cutpoints(static_cast<Eigen::VectorXd::Index>(C));
-            (void) cutpoints;  // dummy to suppress unused var warning
-
-            stan::math::initialize(cutpoints, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(cutpoints,DUMMY_VAR__);
-            fun_scalar_t__ running_sum;
-            (void) running_sum;  // dummy to suppress unused var warning
-
-            stan::math::initialize(running_sum, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(running_sum,DUMMY_VAR__);
-            stan::math::assign(running_sum,0);
-
-
-            if (as_bool(logical_eq(link,1))) {
-                for (int c = 1; c <= C; ++c) {
-
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), logit(running_sum));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-                for (int c = 1; c <= C; ++c) {
-
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), inv_Phi(running_sum));
-                }
-            } else if (as_bool(logical_eq(link,3))) {
-                for (int c = 1; c <= C; ++c) {
-
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), -(log(-(log(running_sum)))));
-                }
-            } else if (as_bool(logical_eq(link,4))) {
-                for (int c = 1; c <= C; ++c) {
-
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), log(-(log1m(running_sum))));
-                }
-            } else if (as_bool(logical_eq(link,5))) {
-                for (int c = 1; c <= C; ++c) {
-
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), tan((stan::math::pi() * (running_sum - 0.5))));
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(scale,cutpoints));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_cutpoints_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& probabilities,
-                   const T1__& scale,
-                   const int& link, std::ostream* pstream__) const {
-        return make_cutpoints(probabilities, scale, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__, T2__>::type
-draw_ystar_rng(const T0__& lower,
-                   const T1__& upper,
-                   const T2__& eta,
-                   const int& link, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int iter(0);
-            (void) iter;  // dummy to suppress unused var warning
-
-            stan::math::fill(iter, std::numeric_limits<int>::min());
-            stan::math::assign(iter,0);
-            fun_scalar_t__ ystar;
-            (void) ystar;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ystar, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ystar,DUMMY_VAR__);
-            stan::math::assign(ystar,stan::math::not_a_number());
-
-
-            if (as_bool(logical_gte(lower,upper))) {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "lower must be less than upper";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            if (as_bool(logical_eq(link,1))) {
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    stan::math::assign(ystar, logistic_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    stan::math::assign(ystar, normal_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,3))) {
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    stan::math::assign(ystar, gumbel_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,4))) {
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    stan::math::assign(ystar, log(-(log1m(uniform_rng(0,1, base_rng__)))));
-                }
-            } else if (as_bool(logical_eq(link,5))) {
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    stan::math::assign(ystar, cauchy_rng(eta,1, base_rng__));
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ystar);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct draw_ystar_rng_functor__ {
-    template <typename T0__, typename T1__, typename T2__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__>::type
-    operator()(const T0__& lower,
-                   const T1__& upper,
-                   const T2__& eta,
-                   const int& link, RNG& base_rng__, std::ostream* pstream__) const {
-        return draw_ystar_rng(lower, upper, eta, link, base_rng__, pstream__);
-    }
-};
-
-class model_polr : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    int J;
-    vector<int> y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    double regularization;
-    vector_d prior_counts;
-    int is_skewed;
-    double shape;
-    double rate;
-    int do_residuals;
-    double half_K;
-    double sqrt_Nm1;
-    int is_constant;
-    vector_d beta_smooth;
-public:
-    model_polr(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_polr(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_polr_namespace::model_polr";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "J", "int", context__.to_vec());
-        J = int(0);
-        vals_i__ = context__.vals_i("J");
-        pos__ = 0;
-        J = vals_i__[pos__++];
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec());
-        regularization = double(0);
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        regularization = vals_r__[pos__++];
-        validate_non_negative_index("prior_counts", "J", J);
-        context__.validate_dims("data initialization", "prior_counts", "vector_d", context__.to_vec(J));
-        validate_non_negative_index("prior_counts", "J", J);
-        prior_counts = vector_d(static_cast<Eigen::VectorXd::Index>(J));
-        vals_r__ = context__.vals_r("prior_counts");
-        pos__ = 0;
-        size_t prior_counts_i_vec_lim__ = J;
-        for (size_t i_vec__ = 0; i_vec__ < prior_counts_i_vec_lim__; ++i_vec__) {
-            prior_counts[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "is_skewed", "int", context__.to_vec());
-        is_skewed = int(0);
-        vals_i__ = context__.vals_i("is_skewed");
-        pos__ = 0;
-        is_skewed = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "shape", "double", context__.to_vec());
-        shape = double(0);
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        shape = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "rate", "double", context__.to_vec());
-        rate = double(0);
-        vals_r__ = context__.vals_r("rate");
-        pos__ = 0;
-        rate = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "do_residuals", "int", context__.to_vec());
-        do_residuals = int(0);
-        vals_i__ = context__.vals_i("do_residuals");
-        pos__ = 0;
-        do_residuals = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"J",J,2);
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],1);
-            check_less_or_equal(function__,"y[k0__]",y[k0__],J);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"regularization",regularization,0);
-        check_greater_or_equal(function__,"prior_counts",prior_counts,0);
-        check_greater_or_equal(function__,"is_skewed",is_skewed,0);
-        check_less_or_equal(function__,"is_skewed",is_skewed,1);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"rate",rate,0);
-        check_greater_or_equal(function__,"do_residuals",do_residuals,0);
-        check_less_or_equal(function__,"do_residuals",do_residuals,1);
-        // initialize data variables
-        half_K = double(0);
-        stan::math::fill(half_K,DUMMY_VAR__);
-        stan::math::assign(half_K,(0.5 * K));
-        sqrt_Nm1 = double(0);
-        stan::math::fill(sqrt_Nm1,DUMMY_VAR__);
-        stan::math::assign(sqrt_Nm1,sqrt((N - 1.0)));
-        is_constant = int(0);
-        stan::math::fill(is_constant, std::numeric_limits<int>::min());
-        stan::math::assign(is_constant,1);
-        validate_non_negative_index("beta_smooth", "0", 0);
-        beta_smooth = vector_d(static_cast<Eigen::VectorXd::Index>(0));
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-
-        try {
-            for (int j = 1; j <= J; ++j) {
-                if (as_bool(logical_neq(get_base1(prior_counts,j,"prior_counts",1),1))) {
-                    stan::math::assign(is_constant, 0);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        check_greater_or_equal(function__,"half_K",half_K,0);
-        check_greater_or_equal(function__,"sqrt_Nm1",sqrt_Nm1,0);
-        check_greater_or_equal(function__,"is_constant",is_constant,0);
-        check_less_or_equal(function__,"is_constant",is_constant,1);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("pi", "J", J);
-        num_params_r__ += (J - 1);
-        validate_non_negative_index("u", "K", K);
-        num_params_r__ += (K);
-        ++num_params_r__;
-        validate_non_negative_index("alpha", "is_skewed", is_skewed);
-        num_params_r__ += is_skewed;
-    }
-
-    ~model_polr() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("pi")))
-            throw std::runtime_error("variable pi missing");
-        vals_r__ = context__.vals_r("pi");
-        pos__ = 0U;
-        validate_non_negative_index("pi", "J", J);
-        context__.validate_dims("initialization", "pi", "vector_d", context__.to_vec(J));
-        // generate_declaration pi
-        vector_d pi(static_cast<Eigen::VectorXd::Index>(J));
-        for (int j1__ = 0U; j1__ < J; ++j1__)
-            pi(j1__) = vals_r__[pos__++];
-        try {
-            writer__.simplex_unconstrain(pi);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable pi: ") + e.what());
-        }
-
-        if (!(context__.contains_r("u")))
-            throw std::runtime_error("variable u missing");
-        vals_r__ = context__.vals_r("u");
-        pos__ = 0U;
-        validate_non_negative_index("u", "K", K);
-        context__.validate_dims("initialization", "u", "vector_d", context__.to_vec(K));
-        // generate_declaration u
-        vector_d u(static_cast<Eigen::VectorXd::Index>(K));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            u(j1__) = vals_r__[pos__++];
-        try {
-            writer__.unit_vector_unconstrain(u);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable u: ") + e.what());
-        }
-
-        if (!(context__.contains_r("R2")))
-            throw std::runtime_error("variable R2 missing");
-        vals_r__ = context__.vals_r("R2");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "R2", "double", context__.to_vec());
-        // generate_declaration R2
-        double R2(0);
-        R2 = vals_r__[pos__++];
-        try {
-            writer__.scalar_lub_unconstrain(0,1,R2);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable R2: ") + e.what());
-        }
-
-        if (!(context__.contains_r("alpha")))
-            throw std::runtime_error("variable alpha missing");
-        vals_r__ = context__.vals_r("alpha");
-        pos__ = 0U;
-        validate_non_negative_index("alpha", "is_skewed", is_skewed);
-        context__.validate_dims("initialization", "alpha", "double", context__.to_vec(is_skewed));
-        // generate_declaration alpha
-        std::vector<double> alpha(is_skewed,double(0));
-        for (int i0__ = 0U; i0__ < is_skewed; ++i0__)
-            alpha[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < is_skewed; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,alpha[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable alpha: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  pi;
-        (void) pi;  // dummy to suppress unused var warning
-        if (jacobian__)
-            pi = in__.simplex_constrain(J,lp__);
-        else
-            pi = in__.simplex_constrain(J);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  u;
-        (void) u;  // dummy to suppress unused var warning
-        if (jacobian__)
-            u = in__.unit_vector_constrain(K,lp__);
-        else
-            u = in__.unit_vector_constrain(K);
-
-        T__ R2;
-        (void) R2;  // dummy to suppress unused var warning
-        if (jacobian__)
-            R2 = in__.scalar_lub_constrain(0,1,lp__);
-        else
-            R2 = in__.scalar_lub_constrain(0,1);
-
-        vector<T__> alpha;
-        size_t dim_alpha_0__ = is_skewed;
-        alpha.reserve(dim_alpha_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_alpha_0__; ++k_0__) {
-            if (jacobian__)
-                alpha.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                alpha.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("cutpoints", "(J - 1)", (J - 1));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  cutpoints(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) cutpoints;  // dummy to suppress unused var warning
-
-        stan::math::initialize(cutpoints, DUMMY_VAR__);
-        stan::math::fill(cutpoints,DUMMY_VAR__);
-
-
-        try {
-            {
-                T__ Delta_y;
-                (void) Delta_y;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Delta_y, DUMMY_VAR__);
-                stan::math::fill(Delta_y,DUMMY_VAR__);
-                stan::math::assign(Delta_y,inv(sqrt((1 - R2))));
-
-
-                if (as_bool(logical_gt(K,1))) {
-                    stan::math::assign(beta, multiply(multiply(multiply(u,sqrt(R2)),Delta_y),sqrt_Nm1));
-                } else {
-                    stan::math::assign(get_base1_lhs(beta,1,"beta",1), (((get_base1(u,1,"u",1) * sqrt(R2)) * Delta_y) * sqrt_Nm1));
-                }
-                stan::math::assign(cutpoints, make_cutpoints(pi,Delta_y,link, pstream__));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (J - 1); ++i0__) {
-            if (stan::math::is_uninitialized(cutpoints(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: cutpoints" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    if (as_bool(logical_eq(is_skewed,0))) {
-                        lp_accum__.add(pw_polr(y,eta,cutpoints,link,1.0, pstream__));
-                    } else {
-                        lp_accum__.add(pw_polr(y,eta,cutpoints,link,get_base1(alpha,1,"alpha",1), pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-
-                    if (as_bool(logical_eq(is_skewed,0))) {
-                        lp_accum__.add(dot_product(weights,pw_polr(y,eta,cutpoints,link,1.0, pstream__)));
-                    } else {
-                        lp_accum__.add(dot_product(weights,pw_polr(y,eta,cutpoints,link,get_base1(alpha,1,"alpha",1), pstream__)));
-                    }
-                }
-                if (as_bool(logical_eq(is_constant,0))) {
-                    lp_accum__.add(dirichlet_log(pi,prior_counts));
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(beta_log(R2,half_K,regularization));
-                }
-                if (as_bool(logical_eq(is_skewed,1))) {
-                    lp_accum__.add(gamma_log(alpha,shape,rate));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("pi");
-        names__.push_back("u");
-        names__.push_back("R2");
-        names__.push_back("alpha");
-        names__.push_back("beta");
-        names__.push_back("cutpoints");
-        names__.push_back("mean_PPD");
-        names__.push_back("residuals");
-        names__.push_back("zeta");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(is_skewed);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J - 1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(J,2) ? J : 1 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((do_residuals ? N : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J - 1));
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_polr_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector_d pi = in__.simplex_constrain(J);
-        vector_d u = in__.unit_vector_constrain(K);
-        double R2 = in__.scalar_lub_constrain(0,1);
-        vector<double> alpha;
-        size_t dim_alpha_0__ = is_skewed;
-        for (size_t k_0__ = 0; k_0__ < dim_alpha_0__; ++k_0__) {
-            alpha.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(pi[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(u[k_0__]);
-        }
-        vars__.push_back(R2);
-        for (int k_0__ = 0; k_0__ < is_skewed; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("cutpoints", "(J - 1)", (J - 1));
-        vector_d cutpoints(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) cutpoints;  // dummy to suppress unused var warning
-
-        stan::math::initialize(cutpoints, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(cutpoints,DUMMY_VAR__);
-
-
-        try {
-            {
-                double Delta_y(0.0);
-                (void) Delta_y;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Delta_y, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Delta_y,DUMMY_VAR__);
-                stan::math::assign(Delta_y,inv(sqrt((1 - R2))));
-
-
-                if (as_bool(logical_gt(K,1))) {
-                    stan::math::assign(beta, multiply(multiply(multiply(u,sqrt(R2)),Delta_y),sqrt_Nm1));
-                } else {
-                    stan::math::assign(get_base1_lhs(beta,1,"beta",1), (((get_base1(u,1,"u",1) * sqrt(R2)) * Delta_y) * sqrt_Nm1));
-                }
-                stan::math::assign(cutpoints, make_cutpoints(pi,Delta_y,link, pstream__));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J - 1); ++k_0__) {
-            vars__.push_back(cutpoints[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("mean_PPD", "(logical_gt(J,2) ? J : 1 )", (logical_gt(J,2) ? J : 1 ));
-        vector_d mean_PPD(static_cast<Eigen::VectorXd::Index>((logical_gt(J,2) ? J : 1 )));
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,rep_vector(0,(logical_gt(J,2) ? J : 1 )));
-        validate_non_negative_index("residuals", "(do_residuals ? N : 0 )", (do_residuals ? N : 0 ));
-        vector_d residuals(static_cast<Eigen::VectorXd::Index>((do_residuals ? N : 0 )));
-        (void) residuals;  // dummy to suppress unused var warning
-
-        stan::math::initialize(residuals, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(residuals,DUMMY_VAR__);
-        validate_non_negative_index("zeta", "(J - 1)", (J - 1));
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) zeta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(zeta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(zeta,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(dense_X)) {
-                stan::math::assign(zeta, add(cutpoints,dot_product(xbar,beta)));
-            } else {
-                stan::math::assign(zeta, cutpoints);
-            }
-            if (as_bool(logical_eq(J,2))) {
-                stan::math::assign(zeta, minus(zeta));
-            }
-            {
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_gt(K,0))) {
-
-                    if (as_bool(dense_X)) {
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                if (as_bool(logical_eq(has_offset,1))) {
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                if (as_bool(K_smooth)) {
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                for (int n = 1; n <= N; ++n) {
-                    {
-                        int y_tilde(0);
-                        (void) y_tilde;  // dummy to suppress unused var warning
-
-                        stan::math::fill(y_tilde, std::numeric_limits<int>::min());
-                        validate_non_negative_index("theta", "J", J);
-                        vector_d theta(static_cast<Eigen::VectorXd::Index>(J));
-                        (void) theta;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(theta, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(theta,DUMMY_VAR__);
-                        double previous(0.0);
-                        (void) previous;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(previous, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(previous,DUMMY_VAR__);
-
-
-                        stan::math::assign(get_base1_lhs(theta,1,"theta",1), CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                        stan::math::assign(previous, get_base1(theta,1,"theta",1));
-                        if (as_bool(is_skewed)) {
-                            stan::math::assign(get_base1_lhs(theta,1,"theta",1), pow(get_base1(theta,1,"theta",1),get_base1(alpha,1,"alpha",1)));
-                        }
-                        for (int j = 2; j <= (J - 1); ++j) {
-                            {
-                                double current(0.0);
-                                (void) current;  // dummy to suppress unused var warning
-
-                                stan::math::initialize(current, std::numeric_limits<double>::quiet_NaN());
-                                stan::math::fill(current,DUMMY_VAR__);
-
-
-                                stan::math::assign(current, CDF_polr((get_base1(cutpoints,j,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                                stan::math::assign(get_base1_lhs(theta,j,"theta",1), (current - previous));
-                                stan::math::assign(previous, current);
-                            }
-                        }
-                        if (as_bool(logical_eq(is_skewed,0))) {
-                            stan::math::assign(get_base1_lhs(theta,J,"theta",1), (1 - previous));
-                        } else {
-                            stan::math::assign(get_base1_lhs(theta,J,"theta",1), (1 - pow(previous,get_base1(alpha,1,"alpha",1))));
-                        }
-                        if (as_bool((primitive_value(logical_lte(previous,0)) || primitive_value(logical_gte(previous,1))))) {
-
-                        } else if (as_bool(logical_eq(J,2))) {
-
-                            stan::math::assign(get_base1_lhs(mean_PPD,1,"mean_PPD",1), (get_base1(mean_PPD,1,"mean_PPD",1) + bernoulli_rng(get_base1(theta,J,"theta",1), base_rng__)));
-                        } else {
-
-                            stan::math::assign(y_tilde, categorical_rng(theta, base_rng__));
-                            stan::math::assign(get_base1_lhs(mean_PPD,y_tilde,"mean_PPD",1), (get_base1(mean_PPD,y_tilde,"mean_PPD",1) + 1));
-                        }
-                        if (as_bool(do_residuals)) {
-                            {
-                                double ystar(0.0);
-                                (void) ystar;  // dummy to suppress unused var warning
-
-                                stan::math::initialize(ystar, std::numeric_limits<double>::quiet_NaN());
-                                stan::math::fill(ystar,DUMMY_VAR__);
-
-
-                                if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                                    stan::math::assign(ystar, draw_ystar_rng(stan::math::negative_infinity(),get_base1(cutpoints,1,"cutpoints",1),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                                    stan::math::assign(ystar, draw_ystar_rng(get_base1(cutpoints,(J - 1),"cutpoints",1),stan::math::positive_infinity(),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                } else {
-                                    stan::math::assign(ystar, draw_ystar_rng(get_base1(cutpoints,(get_base1(y,n,"y",1) - 1),"cutpoints",1),get_base1(cutpoints,get_base1(y,n,"y",1),"cutpoints",1),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                }
-                                stan::math::assign(get_base1_lhs(residuals,n,"residuals",1), (ystar - get_base1(eta,n,"eta",1)));
-                            }
-                        }
-                    }
-                }
-                stan::math::assign(mean_PPD, divide(mean_PPD,N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            vars__.push_back(mean_PPD[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (do_residuals ? N : 0 ); ++k_0__) {
-            vars__.push_back(residuals[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J - 1); ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_polr";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "pi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "u" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "R2";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= is_skewed; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "cutpoints" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (do_residuals ? N : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "residuals" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "pi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "u" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "R2";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= is_skewed; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "cutpoints" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (do_residuals ? N : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "residuals" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_spatial_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_spatial");
-    reader.add_event(968, 968, "end", "model_spatial");
-    return reader;
-}
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gauss_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gauss(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(subtract((-(0.5) * log((6.2831853071795862 * sigma))),multiply(0.5,square(divide(subtract(y,linkinv_gauss(eta,link, pstream__)),sigma)))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gauss_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gauss(y, eta, sigma, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gamma_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gamma(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-GammaReg(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ ret;
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-            stan::math::assign(ret,((rows(y) * ((shape * log(shape)) - stan::math::lgamma(shape))) + ((shape - 1) * sum_log_y)));
-
-
-            if (as_bool(logical_eq(link,2))) {
-                stan::math::assign(ret, ((ret - (shape * sum(eta))) - (shape * sum(elt_divide(y,exp(eta))))));
-            } else if (as_bool(logical_eq(link,1))) {
-                stan::math::assign(ret, ((ret - (shape * sum(log(eta)))) - (shape * sum(elt_divide(y,eta)))));
-            } else if (as_bool(logical_eq(link,3))) {
-                stan::math::assign(ret, ((ret + (shape * sum(log(eta)))) - (shape * dot_product(eta,y))));
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct GammaReg_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) const {
-        return GammaReg(y, eta, shape, link, sum_log_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,3))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape * get_base1(eta,n,"eta",1))));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / exp(get_base1(eta,n,"eta",1)))));
-                }
-            } else if (as_bool(logical_eq(link,1))) {
-
-                for (int n = 1; n <= N; ++n) {
-
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / get_base1(eta,n,"eta",1))));
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gamma_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gamma(y, eta, shape, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_sqrt(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_inv_gaussian_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) const {
-        return linkinv_inv_gaussian(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(((((0.5 * rows(y)) * log((lambda / 6.2831853071795862))) - (1.5 * sum_log_y)) - ((0.5 * lambda) * dot_self(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y))))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return inv_gaussian(y, mu, lambda, sum_log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-pw_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_inv_gaussian(eta,link, pstream__));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(add(multiply((-(0.5) * lambda),square(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y)))),(0.5 * log((lambda / 6.2831853071795862)))),multiply(1.5,log_y)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return pw_inv_gaussian(y, eta, lambda, link, log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-inv_gaussian_rng(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ mu2;
-            (void) mu2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu2,DUMMY_VAR__);
-            stan::math::assign(mu2,square(mu));
-            fun_scalar_t__ z;
-            (void) z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z,DUMMY_VAR__);
-            stan::math::assign(z,uniform_rng(0,1, base_rng__));
-            fun_scalar_t__ y;
-            (void) y;  // dummy to suppress unused var warning
-
-            stan::math::initialize(y, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(y,DUMMY_VAR__);
-            stan::math::assign(y,square(normal_rng(0,1, base_rng__)));
-            fun_scalar_t__ x;
-            (void) x;  // dummy to suppress unused var warning
-
-            stan::math::initialize(x, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(x,DUMMY_VAR__);
-            stan::math::assign(x,(mu + (((mu2 * y) - (mu * sqrt(((((4 * mu) * lambda) * y) + (mu2 * square(y)))))) / (2 * lambda))));
-
-
-            if (as_bool(logical_lte(z,(mu / (mu + x))))) {
-                return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-            } else {
-                return stan::math::promote_scalar<fun_return_scalar_t__>((mu2 / x));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_rng_functor__ {
-    template <typename T0__, typename T1__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) const {
-        return inv_gaussian_rng(mu, lambda, base_rng__, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(0.5,divide(atan(eta),stan::math::pi())));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,6))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(1,inv_cloglog(minus(eta))));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_beta(eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_z_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) const {
-        return linkinv_beta_z(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-
-
-            for (int n = 1; n <= rows(y); ++n) {
-
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * dispersion),((1 - get_base1(mu,n,"mu",1)) * dispersion)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) const {
-        return pw_beta(y, eta, dispersion, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-            validate_non_negative_index("mu_z", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu_z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu_z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu_z,DUMMY_VAR__);
-            stan::math::assign(mu_z,linkinv_beta_z(eta_z,link_phi, pstream__));
-
-
-            for (int n = 1; n <= rows(y); ++n) {
-
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * get_base1(mu_z,n,"mu_z",1)),((1 - get_base1(mu,n,"mu",1)) * get_base1(mu_z,n,"mu_z",1))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_z_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) const {
-        return pw_beta_z(y, eta, eta_z, link, link_phi, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_binom(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_binom_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_binom(eta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T2__, T_lp__>::type
-ll_binom_lp(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            lp_accum__.add(binomial_logit_log(y,trials,eta));
-        } else if (as_bool(logical_lt(link,4))) {
-            lp_accum__.add(binomial_log(y,trials,linkinv_binom(eta,link, pstream__)));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            for (int n = 1; n <= num_elements(y); ++n) {
-
-                lp_accum__.add((get_base1(y,n,"y",1) * get_base1(eta,n,"eta",1)));
-                lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * log1m_exp(get_base1(eta,n,"eta",1))));
-                lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-            }
-        } else if (as_bool(logical_eq(link,5))) {
-
-            for (int n = 1; n <= num_elements(y); ++n) {
-                {
-                    fun_scalar_t__ neg_exp_eta;
-                    (void) neg_exp_eta;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(neg_exp_eta, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(neg_exp_eta,DUMMY_VAR__);
-                    stan::math::assign(neg_exp_eta,-(exp(get_base1(eta,n,"eta",1))));
-
-
-                    lp_accum__.add((get_base1(y,n,"y",1) * log1m_exp(neg_exp_eta)));
-                    lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * neg_exp_eta));
-                    lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-                }
-            }
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_binom_lp_functor__ {
-    template <typename T2__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T2__, T_lp__>::type
-    operator()(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_binom_lp(y, trials, eta, link, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-pw_binom(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,1))) {
-
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_logit_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_binom(eta,link, pstream__));
-
-
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_binom_functor__ {
-    template <typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) const {
-        return pw_binom(y, trials, eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_count(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(link,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_count_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_count(eta, link, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_pois(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            if (as_bool(logical_eq(link,1))) {
-                for (int n = 1; n <= N; ++n) {
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log_log(get_base1(y,n,"y",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,3))) {
-                {
-                    validate_non_negative_index("phi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) phi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(phi,DUMMY_VAR__);
-                    stan::math::assign(phi,linkinv_count(eta,link, pstream__));
-
-
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log(get_base1(y,n,"y",1),get_base1(phi,n,"phi",1)));
-                    }
-                }
-            } else {
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_pois_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_pois(y, eta, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-pw_nb(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("rho", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  rho(static_cast<Eigen::VectorXd::Index>(N));
-            (void) rho;  // dummy to suppress unused var warning
-
-            stan::math::initialize(rho, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(rho,DUMMY_VAR__);
-            stan::math::assign(rho,linkinv_count(eta,link, pstream__));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            for (int n = 1; n <= N; ++n) {
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), neg_binomial_2_log(get_base1(y,n,"y",1),get_base1(rho,n,"rho",1),theta));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_nb_functor__ {
-    template <typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) const {
-        return pw_nb(y, eta, theta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    for (int c = 1; c <= r; ++c) {
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            for (int c = 1; c <= nc; ++c) {
-                                for (int r = c; r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            for (int c = 1; c <= nc; ++c) {
-
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    for (int s = 1; s <= nc; ++s) {
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            lp_accum__.add(normal_log(z_b,0,1));
-            lp_accum__.add(normal_log(z_T,0,1));
-            for (int i = 1; i <= t; ++i) {
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            stan::math::assign(nu, (nu - 0.5));
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            stan::math::assign(K, rows(z_beta));
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            for (int k = 1; k <= K; ++k) {
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            if (as_bool(logical_gt(t,0))) {
-                for (int j = 1; j <= N; ++j) {
-                    for (int i = 1; i <= t; ++i) {
-
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-double
-make_lower(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool(logical_eq(family,1))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-        }
-        if (as_bool(logical_eq(family,5))) {
-
-            if (as_bool(logical_eq(link,2))) {
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        if (as_bool((primitive_value(logical_eq(family,2)) || primitive_value(logical_eq(family,3))))) {
-
-            if (as_bool(logical_eq(link,1))) {
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0.0);
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_lower_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_lower(family, link, pstream__);
-    }
-};
-
-double
-make_upper(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,4))))) {
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0.0);
-        }
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::positive_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_upper_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_upper(family, link, pstream__);
-    }
-};
-
-class model_spatial : public prob_grad {
-private:
-    int N;
-    int K;
-    matrix_d X;
-    int family;
-    int link;
-    int is_continuous;
-    int has_aux;
-    int model_type;
-    int has_intercept;
-    vector_d xbar;
-    vector<int> trials;
-    vector<int> y_int;
-    vector_d y_real;
-    int E_n;
-    vector<vector<int> > edges;
-    int order;
-    vector<int> Q_n;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int prior_dist_rho;
-    double prior_mean_rho;
-    double prior_scale_rho;
-    double prior_df_rho;
-    double shape1_rho;
-    double shape2_rho;
-    double scaling_factor;
-    int prior_dist_for_intercept;
-    int prior_dist;
-    int prior_dist_tau;
-    double prior_mean_for_intercept;
-    double prior_scale_for_intercept;
-    double prior_df_for_intercept;
-    vector_d prior_mean;
-    vector_d prior_scale;
-    vector_d prior_df;
-    double prior_mean_tau;
-    double prior_scale_tau;
-    double prior_df_tau;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int prior_dist_for_aux;
-    double prior_mean_for_aux;
-    double prior_scale_for_aux;
-    double prior_df_for_aux;
-    double poisson_max;
-    int hs;
-    double sum_log_y;
-public:
-    model_spatial(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_spatial(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_spatial_namespace::model_spatial";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(N,K));
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                X(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "is_continuous", "int", context__.to_vec());
-        is_continuous = int(0);
-        vals_i__ = context__.vals_i("is_continuous");
-        pos__ = 0;
-        is_continuous = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_aux", "int", context__.to_vec());
-        has_aux = int(0);
-        vals_i__ = context__.vals_i("has_aux");
-        pos__ = 0;
-        has_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "model_type", "int", context__.to_vec());
-        model_type = int(0);
-        vals_i__ = context__.vals_i("model_type");
-        pos__ = 0;
-        model_type = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("trials", "(logical_eq(family,4) ? N : 0 )", (logical_eq(family,4) ? N : 0 ));
-        context__.validate_dims("data initialization", "trials", "int", context__.to_vec((logical_eq(family,4) ? N : 0 )));
-        validate_non_negative_index("trials", "(logical_eq(family,4) ? N : 0 )", (logical_eq(family,4) ? N : 0 ));
-        trials = std::vector<int>((logical_eq(family,4) ? N : 0 ),int(0));
-        vals_i__ = context__.vals_i("trials");
-        pos__ = 0;
-        size_t trials_limit_0__ = (logical_eq(family,4) ? N : 0 );
-        for (size_t i_0__ = 0; i_0__ < trials_limit_0__; ++i_0__) {
-            trials[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y_int", "(logical_eq(is_continuous,1) ? 0 : N )", (logical_eq(is_continuous,1) ? 0 : N ));
-        context__.validate_dims("data initialization", "y_int", "int", context__.to_vec((logical_eq(is_continuous,1) ? 0 : N )));
-        validate_non_negative_index("y_int", "(logical_eq(is_continuous,1) ? 0 : N )", (logical_eq(is_continuous,1) ? 0 : N ));
-        y_int = std::vector<int>((logical_eq(is_continuous,1) ? 0 : N ),int(0));
-        vals_i__ = context__.vals_i("y_int");
-        pos__ = 0;
-        size_t y_int_limit_0__ = (logical_eq(is_continuous,1) ? 0 : N );
-        for (size_t i_0__ = 0; i_0__ < y_int_limit_0__; ++i_0__) {
-            y_int[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y_real", "(logical_eq(is_continuous,1) ? N : 0 )", (logical_eq(is_continuous,1) ? N : 0 ));
-        context__.validate_dims("data initialization", "y_real", "vector_d", context__.to_vec((logical_eq(is_continuous,1) ? N : 0 )));
-        validate_non_negative_index("y_real", "(logical_eq(is_continuous,1) ? N : 0 )", (logical_eq(is_continuous,1) ? N : 0 ));
-        y_real = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(is_continuous,1) ? N : 0 )));
-        vals_r__ = context__.vals_r("y_real");
-        pos__ = 0;
-        size_t y_real_i_vec_lim__ = (logical_eq(is_continuous,1) ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < y_real_i_vec_lim__; ++i_vec__) {
-            y_real[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "E_n", "int", context__.to_vec());
-        E_n = int(0);
-        vals_i__ = context__.vals_i("E_n");
-        pos__ = 0;
-        E_n = vals_i__[pos__++];
-        validate_non_negative_index("edges", "E_n", E_n);
-        validate_non_negative_index("edges", "2", 2);
-        context__.validate_dims("data initialization", "edges", "int", context__.to_vec(E_n,2));
-        validate_non_negative_index("edges", "E_n", E_n);
-        validate_non_negative_index("edges", "2", 2);
-        edges = std::vector<std::vector<int> >(E_n,std::vector<int>(2,int(0)));
-        vals_i__ = context__.vals_i("edges");
-        pos__ = 0;
-        size_t edges_limit_1__ = 2;
-        for (size_t i_1__ = 0; i_1__ < edges_limit_1__; ++i_1__) {
-            size_t edges_limit_0__ = E_n;
-            for (size_t i_0__ = 0; i_0__ < edges_limit_0__; ++i_0__) {
-                edges[i_0__][i_1__] = vals_i__[pos__++];
-            }
-        }
-        context__.validate_dims("data initialization", "order", "int", context__.to_vec());
-        order = int(0);
-        vals_i__ = context__.vals_i("order");
-        pos__ = 0;
-        order = vals_i__[pos__++];
-        validate_non_negative_index("Q_n", "logical_eq(order,2)", logical_eq(order,2));
-        context__.validate_dims("data initialization", "Q_n", "int", context__.to_vec(logical_eq(order,2)));
-        validate_non_negative_index("Q_n", "logical_eq(order,2)", logical_eq(order,2));
-        Q_n = std::vector<int>(logical_eq(order,2),int(0));
-        vals_i__ = context__.vals_i("Q_n");
-        pos__ = 0;
-        size_t Q_n_limit_0__ = logical_eq(order,2);
-        for (size_t i_0__ = 0; i_0__ < Q_n_limit_0__; ++i_0__) {
-            Q_n[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("w", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        validate_non_negative_index("w", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        w = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        validate_non_negative_index("v", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        v = std::vector<int>((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_eq(order,2) ? (N + 1) : 0 )", (logical_eq(order,2) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_eq(order,2) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_eq(order,2) ? (N + 1) : 0 )", (logical_eq(order,2) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_eq(order,2) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_eq(order,2) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_rho", "int", context__.to_vec());
-        prior_dist_rho = int(0);
-        vals_i__ = context__.vals_i("prior_dist_rho");
-        pos__ = 0;
-        prior_dist_rho = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_rho", "double", context__.to_vec());
-        prior_mean_rho = double(0);
-        vals_r__ = context__.vals_r("prior_mean_rho");
-        pos__ = 0;
-        prior_mean_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_rho", "double", context__.to_vec());
-        prior_scale_rho = double(0);
-        vals_r__ = context__.vals_r("prior_scale_rho");
-        pos__ = 0;
-        prior_scale_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_rho", "double", context__.to_vec());
-        prior_df_rho = double(0);
-        vals_r__ = context__.vals_r("prior_df_rho");
-        pos__ = 0;
-        prior_df_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "shape1_rho", "double", context__.to_vec());
-        shape1_rho = double(0);
-        vals_r__ = context__.vals_r("shape1_rho");
-        pos__ = 0;
-        shape1_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "shape2_rho", "double", context__.to_vec());
-        shape2_rho = double(0);
-        vals_r__ = context__.vals_r("shape2_rho");
-        pos__ = 0;
-        shape2_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "scaling_factor", "double", context__.to_vec());
-        scaling_factor = double(0);
-        vals_r__ = context__.vals_r("scaling_factor");
-        pos__ = 0;
-        scaling_factor = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_tau", "int", context__.to_vec());
-        prior_dist_tau = int(0);
-        vals_i__ = context__.vals_i("prior_dist_tau");
-        pos__ = 0;
-        prior_dist_tau = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_tau", "double", context__.to_vec());
-        prior_mean_tau = double(0);
-        vals_r__ = context__.vals_r("prior_mean_tau");
-        pos__ = 0;
-        prior_mean_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_tau", "double", context__.to_vec());
-        prior_scale_tau = double(0);
-        vals_r__ = context__.vals_r("prior_scale_tau");
-        pos__ = 0;
-        prior_scale_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_tau", "double", context__.to_vec());
-        prior_df_tau = double(0);
-        vals_r__ = context__.vals_r("prior_df_tau");
-        pos__ = 0;
-        prior_df_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"family",family,1);
-        check_less_or_equal(function__,"family",family,5);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"has_aux",has_aux,0);
-        check_less_or_equal(function__,"has_aux",has_aux,1);
-        check_greater_or_equal(function__,"model_type",model_type,1);
-        check_less_or_equal(function__,"model_type",model_type,3);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        for (int k0__ = 0; k0__ < (logical_eq(family,4) ? N : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"trials[k0__]",trials[k0__],0);
-        }
-        check_greater_or_equal(function__,"order",order,1);
-        check_less_or_equal(function__,"order",order,2);
-        for (int k0__ = 0; k0__ < logical_eq(order,2); ++k0__) {
-            check_greater_or_equal(function__,"Q_n[k0__]",Q_n[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_dist_rho",prior_dist_rho,0);
-        check_less_or_equal(function__,"prior_dist_rho",prior_dist_rho,1);
-        check_greater_or_equal(function__,"prior_scale_rho",prior_scale_rho,0);
-        check_greater_or_equal(function__,"prior_df_rho",prior_df_rho,0);
-        check_greater_or_equal(function__,"shape1_rho",shape1_rho,0);
-        check_greater_or_equal(function__,"shape2_rho",shape2_rho,0);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_greater_or_equal(function__,"prior_dist_tau",prior_dist_tau,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_scale_tau",prior_scale_tau,0);
-        check_greater_or_equal(function__,"prior_df_tau",prior_df_tau,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        // initialize data variables
-        poisson_max = double(0);
-        stan::math::fill(poisson_max,DUMMY_VAR__);
-        stan::math::assign(poisson_max,pow(2.0,30.0));
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-        sum_log_y = double(0);
-        stan::math::fill(sum_log_y,DUMMY_VAR__);
-        stan::math::assign(sum_log_y,(logical_eq(family,1) ? stan::math::not_a_number() : sum(log(y_real)) ));
-
-        try {
-            if (as_bool(logical_lte(prior_dist,2))) {
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                stan::math::assign(hs, 4);
-            } else {
-                stan::math::assign(hs, 0);
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "K", K);
-        num_params_r__ += K;
-        validate_non_negative_index("theta_raw", "(logical_eq(model_type,1) ? 0 : N )", (logical_eq(model_type,1) ? 0 : N ));
-        num_params_r__ += (logical_eq(model_type,1) ? 0 : N );
-        validate_non_negative_index("phi_raw", "(N - 1)", (N - 1));
-        num_params_r__ += (N - 1);
-        validate_non_negative_index("rho", "logical_neq(model_type,1)", logical_neq(model_type,1));
-        num_params_r__ += logical_neq(model_type,1);
-        ++num_params_r__;
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("aux_unscaled", "has_aux", has_aux);
-        num_params_r__ += has_aux;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-    }
-
-    ~model_spatial() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "K", K);
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec(K));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>(K));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("theta_raw")))
-            throw std::runtime_error("variable theta_raw missing");
-        vals_r__ = context__.vals_r("theta_raw");
-        pos__ = 0U;
-        validate_non_negative_index("theta_raw", "(logical_eq(model_type,1) ? 0 : N )", (logical_eq(model_type,1) ? 0 : N ));
-        context__.validate_dims("initialization", "theta_raw", "vector_d", context__.to_vec((logical_eq(model_type,1) ? 0 : N )));
-        // generate_declaration theta_raw
-        vector_d theta_raw(static_cast<Eigen::VectorXd::Index>((logical_eq(model_type,1) ? 0 : N )));
-        for (int j1__ = 0U; j1__ < (logical_eq(model_type,1) ? 0 : N ); ++j1__)
-            theta_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(theta_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable theta_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("phi_raw")))
-            throw std::runtime_error("variable phi_raw missing");
-        vals_r__ = context__.vals_r("phi_raw");
-        pos__ = 0U;
-        validate_non_negative_index("phi_raw", "(N - 1)", (N - 1));
-        context__.validate_dims("initialization", "phi_raw", "vector_d", context__.to_vec((N - 1)));
-        // generate_declaration phi_raw
-        vector_d phi_raw(static_cast<Eigen::VectorXd::Index>((N - 1)));
-        for (int j1__ = 0U; j1__ < (N - 1); ++j1__)
-            phi_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(phi_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable phi_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "logical_neq(model_type,1)", logical_neq(model_type,1));
-        context__.validate_dims("initialization", "rho", "double", context__.to_vec(logical_neq(model_type,1)));
-        // generate_declaration rho
-        std::vector<double> rho(logical_neq(model_type,1),double(0));
-        for (int i0__ = 0U; i0__ < logical_neq(model_type,1); ++i0__)
-            rho[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_neq(model_type,1); ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) ),rho[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "tau", "double", context__.to_vec());
-        // generate_declaration tau
-        double tau(0);
-        tau = vals_r__[pos__++];
-        try {
-            writer__.scalar_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        validate_non_negative_index("aux_unscaled", "has_aux", has_aux);
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec(has_aux));
-        // generate_declaration aux_unscaled
-        std::vector<double> aux_unscaled(has_aux,double(0));
-        for (int i0__ = 0U; i0__ < has_aux; ++i0__)
-            aux_unscaled[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_aux; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),lp__));
-            else
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain(K,lp__);
-        else
-            z_beta = in__.vector_constrain(K);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_raw;
-        (void) theta_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ),lp__);
-        else
-            theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  phi_raw;
-        (void) phi_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            phi_raw = in__.vector_constrain((N - 1),lp__);
-        else
-            phi_raw = in__.vector_constrain((N - 1));
-
-        vector<T__> rho;
-        size_t dim_rho_0__ = logical_neq(model_type,1);
-        rho.reserve(dim_rho_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_rho_0__; ++k_0__) {
-            if (jacobian__)
-                rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) ),lp__));
-            else
-                rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) )));
-        }
-
-        T__ tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.scalar_lb_constrain(0,lp__);
-        else
-            tau = in__.scalar_lb_constrain(0);
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = has_aux;
-        aux_unscaled.reserve(dim_aux_unscaled_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            if (jacobian__)
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(has_aux,0) ? stan::math::promote_scalar<T__>(0) : stan::math::promote_scalar<T__>((logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<T__>(get_base1(aux_unscaled,1,"aux_unscaled",1)) : stan::math::promote_scalar<T__>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<T__>(((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)) + prior_mean_for_aux)) : stan::math::promote_scalar<T__>((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1))) )) )) ));
-        validate_non_negative_index("phi", "N", N);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) phi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(phi, DUMMY_VAR__);
-        stan::math::fill(phi,DUMMY_VAR__);
-        validate_non_negative_index("psi", "N", N);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  psi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) psi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(psi, DUMMY_VAR__);
-        stan::math::fill(psi,DUMMY_VAR__);
-
-
-        try {
-            stan::model::assign(phi, 
-                        stan::model::cons_list(stan::model::index_min_max(1, (N - 1)), stan::model::nil_index_list()), 
-                        phi_raw, 
-                        "assigning variable phi");
-            stan::math::assign(get_base1_lhs(phi,N,"phi",1), -(sum(phi_raw)));
-            if (as_bool(logical_eq(model_type,1))) {
-                stan::math::assign(psi, multiply(phi,tau));
-            } else if (as_bool(logical_eq(model_type,2))) {
-                stan::math::assign(psi, add(multiply(phi,get_base1(rho,1,"rho",1)),multiply(theta_raw,tau)));
-            } else if (as_bool(logical_eq(model_type,3))) {
-                stan::math::assign(psi, multiply(tau,add(multiply(sqrt((1 - get_base1(rho,1,"rho",1))),theta_raw),multiply(sqrt((get_base1(rho,1,"rho",1) / scaling_factor)),phi))));
-            }
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < N; ++i0__) {
-            if (stan::math::is_uninitialized(phi(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: phi" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < N; ++i0__) {
-            if (stan::math::is_uninitialized(psi(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: psi" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_eq(has_intercept,1))) {
-                    stan::math::assign(eta, add(add(get_base1(gamma,1,"gamma",1),multiply(X,beta)),psi));
-                } else {
-                    stan::math::assign(eta, add(multiply(X,beta),psi));
-                }
-                if (as_bool(logical_eq(family,1))) {
-
-                    stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    lp_accum__.add(normal_log(y_real,eta,aux));
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    lp_accum__.add(poisson_log(y_int,eta));
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    lp_accum__.add(neg_binomial_2_log(y_int,eta,aux));
-                } else if (as_bool(logical_eq(family,4))) {
-
-                    stan::math::assign(eta, linkinv_binom(eta,link, pstream__));
-                    lp_accum__.add(binomial_log(y_int,trials,eta));
-                } else if (as_bool(logical_eq(family,5))) {
-
-                    lp_accum__.add(GammaReg(y_real,eta,aux,link,sum_log_y, pstream__));
-                }
-                if (as_bool(logical_eq(order,1))) {
-                    lp_accum__.add((-(0.5) * dot_self(subtract(stan::model::rvalue(phi, stan::model::cons_list(stan::model::index_multi(stan::model::rvalue(edges, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "edges")), stan::model::nil_index_list()), "phi"),stan::model::rvalue(phi, stan::model::cons_list(stan::model::index_multi(stan::model::rvalue(edges, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "edges")), stan::model::nil_index_list()), "phi")))));
-                } else if (as_bool(logical_eq(order,2))) {
-                    lp_accum__.add((-(0.5) * dot_product(phi,csr_matrix_times_vector(N,N,w,v,u,phi))));
-                }
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                if (as_bool(logical_eq(model_type,2))) {
-
-                    lp_accum__.add(normal_log(theta_raw,0,1));
-                    if (as_bool(logical_eq(prior_dist_rho,1))) {
-                        lp_accum__.add(normal_log(get_base1(rho,1,"rho",1),prior_mean_rho,prior_scale_rho));
-                    } else if (as_bool(logical_eq(prior_dist_rho,2))) {
-                        lp_accum__.add(student_t_log(get_base1(rho,1,"rho",1),prior_df_rho,prior_mean_rho,prior_scale_rho));
-                    } else if (as_bool(logical_eq(prior_dist_rho,3))) {
-                        lp_accum__.add(exponential_log(get_base1(rho,1,"rho",1),prior_scale_rho));
-                    }
-                } else if (as_bool(logical_eq(model_type,3))) {
-
-                    lp_accum__.add(normal_log(theta_raw,0,1));
-                    if (as_bool(logical_eq(prior_dist_rho,1))) {
-                        lp_accum__.add(beta_log(get_base1(rho,1,"rho",1),shape1_rho,shape2_rho));
-                    }
-                }
-                if (as_bool(logical_eq(prior_dist_tau,1))) {
-                    lp_accum__.add(normal_log(tau,prior_mean_tau,prior_scale_tau));
-                } else if (as_bool(logical_eq(prior_dist_tau,2))) {
-                    lp_accum__.add(student_t_log(tau,prior_df_tau,prior_mean_tau,prior_scale_tau));
-                } else if (as_bool(logical_eq(prior_dist_tau,3))) {
-                    lp_accum__.add(exponential_log(tau,prior_scale_tau));
-                }
-                if (as_bool(logical_eq(has_aux,1))) {
-
-                    if (as_bool((primitive_value(logical_gt(prior_dist_for_aux,0)) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                                lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                                lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                            } else {
-                                lp_accum__.add(exponential_log(aux_unscaled,1));
-                            }
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("theta_raw");
-        names__.push_back("phi_raw");
-        names__.push_back("rho");
-        names__.push_back("tau");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("aux_unscaled");
-        names__.push_back("one_over_lambda");
-        names__.push_back("beta");
-        names__.push_back("aux");
-        names__.push_back("phi");
-        names__.push_back("psi");
-        names__.push_back("mean_PPD");
-        names__.push_back("alpha");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(model_type,1) ? 0 : N ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((N - 1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_neq(model_type,1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_aux);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_spatial_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-        vector_d z_beta = in__.vector_constrain(K);
-        vector_d theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ));
-        vector_d phi_raw = in__.vector_constrain((N - 1));
-        vector<double> rho;
-        size_t dim_rho_0__ = logical_neq(model_type,1);
-        for (size_t k_0__ = 0; k_0__ < dim_rho_0__; ++k_0__) {
-            rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) )));
-        }
-        double tau = in__.scalar_lb_constrain(0);
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = has_aux;
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            vars__.push_back(theta_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (N - 1); ++k_0__) {
-            vars__.push_back(phi_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_neq(model_type,1); ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        vars__.push_back(tau);
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < has_aux; ++k_0__) {
-            vars__.push_back(aux_unscaled[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(has_aux,0) ? stan::math::promote_scalar<double>(0) : stan::math::promote_scalar<double>((logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<double>(get_base1(aux_unscaled,1,"aux_unscaled",1)) : stan::math::promote_scalar<double>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<double>(((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)) + prior_mean_for_aux)) : stan::math::promote_scalar<double>((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1))) )) )) ));
-        validate_non_negative_index("phi", "N", N);
-        vector_d phi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) phi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(phi,DUMMY_VAR__);
-        validate_non_negative_index("psi", "N", N);
-        vector_d psi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) psi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(psi, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(psi,DUMMY_VAR__);
-
-
-        try {
-            stan::model::assign(phi, 
-                        stan::model::cons_list(stan::model::index_min_max(1, (N - 1)), stan::model::nil_index_list()), 
-                        phi_raw, 
-                        "assigning variable phi");
-            stan::math::assign(get_base1_lhs(phi,N,"phi",1), -(sum(phi_raw)));
-            if (as_bool(logical_eq(model_type,1))) {
-                stan::math::assign(psi, multiply(phi,tau));
-            } else if (as_bool(logical_eq(model_type,2))) {
-                stan::math::assign(psi, add(multiply(phi,get_base1(rho,1,"rho",1)),multiply(theta_raw,tau)));
-            } else if (as_bool(logical_eq(model_type,3))) {
-                stan::math::assign(psi, multiply(tau,add(multiply(sqrt((1 - get_base1(rho,1,"rho",1))),theta_raw),multiply(sqrt((get_base1(rho,1,"rho",1) / scaling_factor)),phi))));
-            }
-            if (as_bool(logical_eq(prior_dist,0))) {
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                for (int k = 1; k <= K; ++k) {
-
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    for (int k = 1; k <= K; ++k) {
-
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < N; ++k_0__) {
-            vars__.push_back(phi[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < N; ++k_0__) {
-            vars__.push_back(psi[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-
-
-        try {
-            if (as_bool(has_intercept)) {
-                stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(beta,xbar)));
-            }
-            {
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    stan::math::assign(eta, add(add(get_base1(alpha,1,"alpha",1),multiply(X,beta)),psi));
-                } else {
-
-                    stan::math::assign(eta, add(multiply(X,beta),psi));
-                }
-                if (as_bool(logical_eq(family,1))) {
-
-                    stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),aux, base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-
-                        if (as_bool(logical_lt(get_base1(eta,n,"eta",1),poisson_max))) {
-                            stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(get_base1(eta,n,"eta",1), base_rng__)));
-                        } else {
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),sqrt(get_base1(eta,n,"eta",1)), base_rng__)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double gamma_temp(0.0);
-                            (void) gamma_temp;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(gamma_temp, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(gamma_temp,DUMMY_VAR__);
-
-
-                            if (as_bool(is_inf(aux))) {
-                                stan::math::assign(gamma_temp, get_base1(eta,n,"eta",1));
-                            } else {
-                                stan::math::assign(gamma_temp, gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__));
-                            }
-                            if (as_bool(logical_lt(gamma_temp,poisson_max))) {
-                                stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(gamma_temp, base_rng__)));
-                            } else {
-                                stan::math::assign(mean_PPD, (mean_PPD + normal_rng(gamma_temp,sqrt(gamma_temp), base_rng__)));
-                            }
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,4))) {
-
-                    stan::math::assign(eta, linkinv_binom(eta,link, pstream__));
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + binomial_rng(get_base1(trials,n,"trials",1),inv_logit(get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,5))) {
-
-                    if (as_bool(logical_gt(link,1))) {
-                        stan::math::assign(eta, linkinv_gamma(eta,link, pstream__));
-                    }
-                    for (int n = 1; n <= N; ++n) {
-                        stan::math::assign(mean_PPD, (mean_PPD + gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                }
-            }
-            stan::math::assign(mean_PPD, (mean_PPD / N));
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        vars__.push_back(mean_PPD);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_spatial";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (N - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_neq(model_type,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "tau";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= has_aux; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "psi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (N - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_neq(model_type,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "tau";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= has_aux; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "psi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-#endif
diff --git a/src/include/models.hpp.bak b/src/include/models.hpp.bak
deleted file mode 100644
index e9e431753..000000000
--- a/src/include/models.hpp.bak
+++ /dev/null
@@ -1,22874 +0,0 @@
-/*
-    rstanarm is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    rstanarm is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with rstanarm.  If not, see <http://www.gnu.org/licenses/>.
-*/
-#ifndef MODELS_HPP
-#define MODELS_HPP
-#define STAN__SERVICES__COMMAND_HPP
-#include <rstan/rstaninc.hpp>
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_bernoulli_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_bernoulli");
-    reader.add_event(760, 760, "end", "model_bernoulli");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 52;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 54;
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        current_statement_begin__ = 55;
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        current_statement_begin__ = 57;
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            current_statement_begin__ = 65;
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            current_statement_begin__ = 68;
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            current_statement_begin__ = 69;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 70;
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            current_statement_begin__ = 73;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 74;
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            current_statement_begin__ = 75;
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            current_statement_begin__ = 76;
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            current_statement_begin__ = 77;
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            current_statement_begin__ = 79;
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    current_statement_begin__ = 83;
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    current_statement_begin__ = 84;
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    current_statement_begin__ = 85;
-                                    for (int c = 1; c <= r; ++c) {
-                                        current_statement_begin__ = 85;
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    current_statement_begin__ = 86;
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    current_statement_begin__ = 87;
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 91;
-                            for (int c = 1; c <= nc; ++c) {
-                                current_statement_begin__ = 91;
-                                for (int r = c; r <= nc; ++r) {
-
-                                    current_statement_begin__ = 92;
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    current_statement_begin__ = 93;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 97;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 115;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 117;
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            current_statement_begin__ = 119;
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                current_statement_begin__ = 120;
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            current_statement_begin__ = 121;
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            current_statement_begin__ = 122;
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            current_statement_begin__ = 126;
-                            for (int c = 1; c <= nc; ++c) {
-
-                                current_statement_begin__ = 127;
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                current_statement_begin__ = 128;
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                current_statement_begin__ = 129;
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    current_statement_begin__ = 130;
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    current_statement_begin__ = 131;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 134;
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    current_statement_begin__ = 136;
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    current_statement_begin__ = 137;
-                                    for (int s = 1; s <= nc; ++s) {
-                                        current_statement_begin__ = 137;
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    current_statement_begin__ = 138;
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 142;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            current_statement_begin__ = 165;
-            lp_accum__.add(normal_log(z_b,0,1));
-            current_statement_begin__ = 166;
-            lp_accum__.add(normal_log(z_T,0,1));
-            current_statement_begin__ = 167;
-            for (int i = 1; i <= t; ++i) {
-                current_statement_begin__ = 167;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        current_statement_begin__ = 171;
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        current_statement_begin__ = 172;
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        current_statement_begin__ = 173;
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        current_statement_begin__ = 174;
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            current_statement_begin__ = 175;
-                            stan::math::assign(nu, (nu - 0.5));
-                            current_statement_begin__ = 176;
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            current_statement_begin__ = 177;
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        current_statement_begin__ = 179;
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        current_statement_begin__ = 180;
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 182;
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            current_statement_begin__ = 183;
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            current_statement_begin__ = 200;
-            stan::math::assign(K, rows(z_beta));
-            current_statement_begin__ = 201;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 201;
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            current_statement_begin__ = 202;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 218;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 233;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 233;
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            current_statement_begin__ = 234;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            current_statement_begin__ = 256;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 271;
-            if (as_bool(logical_gt(t,0))) {
-                current_statement_begin__ = 271;
-                for (int j = 1; j <= N; ++j) {
-                    current_statement_begin__ = 271;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 272;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        current_statement_begin__ = 273;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 275;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_bern(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 288;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 288;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 289;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 290;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 291;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 292;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            current_statement_begin__ = 293;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 293;
-        current_statement_begin__ = 294;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_bern_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_bern(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type
-ll_bern_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta0,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta1,
-               const int& link,
-               const std::vector<int>& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 307;
-        if (as_bool(logical_eq(link,1))) {
-
-            current_statement_begin__ = 308;
-            lp_accum__.add(logistic_ccdf_log(eta0,0,1));
-            current_statement_begin__ = 309;
-            lp_accum__.add(logistic_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,2))) {
-
-            current_statement_begin__ = 312;
-            lp_accum__.add(normal_ccdf_log(eta0,0,1));
-            current_statement_begin__ = 313;
-            lp_accum__.add(normal_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,3))) {
-
-            current_statement_begin__ = 316;
-            lp_accum__.add(cauchy_ccdf_log(eta0,0,1));
-            current_statement_begin__ = 317;
-            lp_accum__.add(cauchy_cdf_log(eta1,0,1));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            current_statement_begin__ = 320;
-            lp_accum__.add(log1m_exp(eta0));
-            current_statement_begin__ = 321;
-            lp_accum__.add(eta1);
-        } else if (as_bool(logical_eq(link,5))) {
-
-            current_statement_begin__ = 324;
-            lp_accum__.add(log1m_exp(minus(exp(eta1))));
-            current_statement_begin__ = 325;
-            lp_accum__.add(minus(exp(eta0)));
-        } else {
-            current_statement_begin__ = 327;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 327;
-        current_statement_begin__ = 328;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_bern_lp_functor__ {
-    template <typename T0__, typename T1__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T0__, T1__, T_lp__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta0,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta1,
-               const int& link,
-               const std::vector<int>& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_bern_lp(eta0, eta1, link, N, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_bern(const int& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 343;
-            if (as_bool(logical_eq(link,1))) {
-
-                current_statement_begin__ = 344;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 344;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), bernoulli_logit_log(y,get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_bern(eta,link, pstream__));
-
-
-                    current_statement_begin__ = 348;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 348;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), bernoulli_log(y,get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 350;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 350;
-            current_statement_begin__ = 351;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_bern_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const int& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_bern(y, eta, link, pstream__);
-    }
-};
-
-class model_bernoulli : public prob_grad {
-private:
-    int K;
-    vector<int> N;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X0;
-    vector<matrix_d> X1;
-    int nnz_X0;
-    vector_d w_X0;
-    vector<int> v_X0;
-    vector<int> u_X0;
-    int nnz_X1;
-    vector_d w_X1;
-    vector<int> v_X1;
-    vector<int> u_X1;
-    int K_smooth;
-    matrix_d S0;
-    matrix_d S1;
-    vector<int> smooth_map;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights0;
-    vector_d weights1;
-    int has_offset;
-    vector_d offset0;
-    vector_d offset1;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    vector<int> num_non_zero;
-    vector_d w0;
-    vector_d w1;
-    vector<int> v0;
-    vector<int> v1;
-    vector<int> u0;
-    vector<int> u1;
-    int special_case;
-    int NN;
-    double aux;
-    vector<vector<int> > V0;
-    vector<vector<int> > V1;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_bernoulli(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_bernoulli(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_bernoulli_namespace::model_bernoulli";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("N", "2", 2);
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec(2));
-        validate_non_negative_index("N", "2", 2);
-        N = std::vector<int>(2,int(0));
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        size_t N_limit_0__ = 2;
-        for (size_t i_0__ = 0; i_0__ < N_limit_0__; ++i_0__) {
-            N[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X0", "dense_X", dense_X);
-        validate_non_negative_index("X0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("X0", "K", K);
-        context__.validate_dims("data initialization", "X0", "matrix_d", context__.to_vec(dense_X,get_base1(N,1,"N",1),K));
-        validate_non_negative_index("X0", "dense_X", dense_X);
-        validate_non_negative_index("X0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("X0", "K", K);
-        X0 = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X0");
-        pos__ = 0;
-        size_t X0_m_mat_lim__ = get_base1(N,1,"N",1);
-        size_t X0_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X0_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X0_m_mat_lim__; ++m_mat__) {
-                size_t X0_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X0_limit_0__; ++i_0__) {
-                    X0[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        validate_non_negative_index("X1", "dense_X", dense_X);
-        validate_non_negative_index("X1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("X1", "K", K);
-        context__.validate_dims("data initialization", "X1", "matrix_d", context__.to_vec(dense_X,get_base1(N,2,"N",1),K));
-        validate_non_negative_index("X1", "dense_X", dense_X);
-        validate_non_negative_index("X1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("X1", "K", K);
-        X1 = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X1");
-        pos__ = 0;
-        size_t X1_m_mat_lim__ = get_base1(N,2,"N",1);
-        size_t X1_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X1_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X1_m_mat_lim__; ++m_mat__) {
-                size_t X1_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X1_limit_0__; ++i_0__) {
-                    X1[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X0", "int", context__.to_vec());
-        nnz_X0 = int(0);
-        vals_i__ = context__.vals_i("nnz_X0");
-        pos__ = 0;
-        nnz_X0 = vals_i__[pos__++];
-        validate_non_negative_index("w_X0", "nnz_X0", nnz_X0);
-        context__.validate_dims("data initialization", "w_X0", "vector_d", context__.to_vec(nnz_X0));
-        validate_non_negative_index("w_X0", "nnz_X0", nnz_X0);
-        w_X0 = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X0));
-        vals_r__ = context__.vals_r("w_X0");
-        pos__ = 0;
-        size_t w_X0_i_vec_lim__ = nnz_X0;
-        for (size_t i_vec__ = 0; i_vec__ < w_X0_i_vec_lim__; ++i_vec__) {
-            w_X0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X0", "nnz_X0", nnz_X0);
-        context__.validate_dims("data initialization", "v_X0", "int", context__.to_vec(nnz_X0));
-        validate_non_negative_index("v_X0", "nnz_X0", nnz_X0);
-        v_X0 = std::vector<int>(nnz_X0,int(0));
-        vals_i__ = context__.vals_i("v_X0");
-        pos__ = 0;
-        size_t v_X0_limit_0__ = nnz_X0;
-        for (size_t i_0__ = 0; i_0__ < v_X0_limit_0__; ++i_0__) {
-            v_X0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X0", "(dense_X ? 0 : (get_base1(N,1,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ));
-        context__.validate_dims("data initialization", "u_X0", "int", context__.to_vec((dense_X ? 0 : (get_base1(N,1,"N",1) + 1) )));
-        validate_non_negative_index("u_X0", "(dense_X ? 0 : (get_base1(N,1,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ));
-        u_X0 = std::vector<int>((dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X0");
-        pos__ = 0;
-        size_t u_X0_limit_0__ = (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X0_limit_0__; ++i_0__) {
-            u_X0[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "nnz_X1", "int", context__.to_vec());
-        nnz_X1 = int(0);
-        vals_i__ = context__.vals_i("nnz_X1");
-        pos__ = 0;
-        nnz_X1 = vals_i__[pos__++];
-        validate_non_negative_index("w_X1", "nnz_X1", nnz_X1);
-        context__.validate_dims("data initialization", "w_X1", "vector_d", context__.to_vec(nnz_X1));
-        validate_non_negative_index("w_X1", "nnz_X1", nnz_X1);
-        w_X1 = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X1));
-        vals_r__ = context__.vals_r("w_X1");
-        pos__ = 0;
-        size_t w_X1_i_vec_lim__ = nnz_X1;
-        for (size_t i_vec__ = 0; i_vec__ < w_X1_i_vec_lim__; ++i_vec__) {
-            w_X1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X1", "nnz_X1", nnz_X1);
-        context__.validate_dims("data initialization", "v_X1", "int", context__.to_vec(nnz_X1));
-        validate_non_negative_index("v_X1", "nnz_X1", nnz_X1);
-        v_X1 = std::vector<int>(nnz_X1,int(0));
-        vals_i__ = context__.vals_i("v_X1");
-        pos__ = 0;
-        size_t v_X1_limit_0__ = nnz_X1;
-        for (size_t i_0__ = 0; i_0__ < v_X1_limit_0__; ++i_0__) {
-            v_X1[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X1", "(dense_X ? 0 : (get_base1(N,2,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ));
-        context__.validate_dims("data initialization", "u_X1", "int", context__.to_vec((dense_X ? 0 : (get_base1(N,2,"N",1) + 1) )));
-        validate_non_negative_index("u_X1", "(dense_X ? 0 : (get_base1(N,2,\"N\",1) + 1) )", (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ));
-        u_X1 = std::vector<int>((dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X1");
-        pos__ = 0;
-        size_t u_X1_limit_0__ = (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X1_limit_0__; ++i_0__) {
-            u_X1[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("S0", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S0", "matrix_d", context__.to_vec(get_base1(N,1,"N",1),K_smooth));
-        validate_non_negative_index("S0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        validate_non_negative_index("S0", "K_smooth", K_smooth);
-        S0 = matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S0");
-        pos__ = 0;
-        size_t S0_m_mat_lim__ = get_base1(N,1,"N",1);
-        size_t S0_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S0_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S0_m_mat_lim__; ++m_mat__) {
-                S0(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("S1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("S1", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S1", "matrix_d", context__.to_vec(get_base1(N,2,"N",1),K_smooth));
-        validate_non_negative_index("S1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        validate_non_negative_index("S1", "K_smooth", K_smooth);
-        S1 = matrix_d(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S1");
-        pos__ = 0;
-        size_t S1_m_mat_lim__ = get_base1(N,2,"N",1);
-        size_t S1_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S1_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S1_m_mat_lim__; ++m_mat__) {
-                S1(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights0", "(has_weights ? get_base1(N,1,\"N\",1) : 0 )", (has_weights ? get_base1(N,1,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "weights0", "vector_d", context__.to_vec((has_weights ? get_base1(N,1,"N",1) : 0 )));
-        validate_non_negative_index("weights0", "(has_weights ? get_base1(N,1,\"N\",1) : 0 )", (has_weights ? get_base1(N,1,"N",1) : 0 ));
-        weights0 = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? get_base1(N,1,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("weights0");
-        pos__ = 0;
-        size_t weights0_i_vec_lim__ = (has_weights ? get_base1(N,1,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights0_i_vec_lim__; ++i_vec__) {
-            weights0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("weights1", "(has_weights ? get_base1(N,2,\"N\",1) : 0 )", (has_weights ? get_base1(N,2,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "weights1", "vector_d", context__.to_vec((has_weights ? get_base1(N,2,"N",1) : 0 )));
-        validate_non_negative_index("weights1", "(has_weights ? get_base1(N,2,\"N\",1) : 0 )", (has_weights ? get_base1(N,2,"N",1) : 0 ));
-        weights1 = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? get_base1(N,2,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("weights1");
-        pos__ = 0;
-        size_t weights1_i_vec_lim__ = (has_weights ? get_base1(N,2,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights1_i_vec_lim__; ++i_vec__) {
-            weights1[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset0", "(has_offset ? get_base1(N,1,\"N\",1) : 0 )", (has_offset ? get_base1(N,1,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "offset0", "vector_d", context__.to_vec((has_offset ? get_base1(N,1,"N",1) : 0 )));
-        validate_non_negative_index("offset0", "(has_offset ? get_base1(N,1,\"N\",1) : 0 )", (has_offset ? get_base1(N,1,"N",1) : 0 ));
-        offset0 = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? get_base1(N,1,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("offset0");
-        pos__ = 0;
-        size_t offset0_i_vec_lim__ = (has_offset ? get_base1(N,1,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset0_i_vec_lim__; ++i_vec__) {
-            offset0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("offset1", "(has_offset ? get_base1(N,2,\"N\",1) : 0 )", (has_offset ? get_base1(N,2,"N",1) : 0 ));
-        context__.validate_dims("data initialization", "offset1", "vector_d", context__.to_vec((has_offset ? get_base1(N,2,"N",1) : 0 )));
-        validate_non_negative_index("offset1", "(has_offset ? get_base1(N,2,\"N\",1) : 0 )", (has_offset ? get_base1(N,2,"N",1) : 0 ));
-        offset1 = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? get_base1(N,2,"N",1) : 0 )));
-        vals_r__ = context__.vals_r("offset1");
-        pos__ = 0;
-        size_t offset1_i_vec_lim__ = (has_offset ? get_base1(N,2,"N",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset1_i_vec_lim__; ++i_vec__) {
-            offset1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("num_non_zero", "2", 2);
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec(2));
-        validate_non_negative_index("num_non_zero", "2", 2);
-        num_non_zero = std::vector<int>(2,int(0));
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        size_t num_non_zero_limit_0__ = 2;
-        for (size_t i_0__ = 0; i_0__ < num_non_zero_limit_0__; ++i_0__) {
-            num_non_zero[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("w0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        context__.validate_dims("data initialization", "w0", "vector_d", context__.to_vec(get_base1(num_non_zero,1,"num_non_zero",1)));
-        validate_non_negative_index("w0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        w0 = vector_d(static_cast<Eigen::VectorXd::Index>(get_base1(num_non_zero,1,"num_non_zero",1)));
-        vals_r__ = context__.vals_r("w0");
-        pos__ = 0;
-        size_t w0_i_vec_lim__ = get_base1(num_non_zero,1,"num_non_zero",1);
-        for (size_t i_vec__ = 0; i_vec__ < w0_i_vec_lim__; ++i_vec__) {
-            w0[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("w1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        context__.validate_dims("data initialization", "w1", "vector_d", context__.to_vec(get_base1(num_non_zero,2,"num_non_zero",1)));
-        validate_non_negative_index("w1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        w1 = vector_d(static_cast<Eigen::VectorXd::Index>(get_base1(num_non_zero,2,"num_non_zero",1)));
-        vals_r__ = context__.vals_r("w1");
-        pos__ = 0;
-        size_t w1_i_vec_lim__ = get_base1(num_non_zero,2,"num_non_zero",1);
-        for (size_t i_vec__ = 0; i_vec__ < w1_i_vec_lim__; ++i_vec__) {
-            w1[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        context__.validate_dims("data initialization", "v0", "int", context__.to_vec(get_base1(num_non_zero,1,"num_non_zero",1)));
-        validate_non_negative_index("v0", "get_base1(num_non_zero,1,\"num_non_zero\",1)", get_base1(num_non_zero,1,"num_non_zero",1));
-        v0 = std::vector<int>(get_base1(num_non_zero,1,"num_non_zero",1),int(0));
-        vals_i__ = context__.vals_i("v0");
-        pos__ = 0;
-        size_t v0_limit_0__ = get_base1(num_non_zero,1,"num_non_zero",1);
-        for (size_t i_0__ = 0; i_0__ < v0_limit_0__; ++i_0__) {
-            v0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("v1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        context__.validate_dims("data initialization", "v1", "int", context__.to_vec(get_base1(num_non_zero,2,"num_non_zero",1)));
-        validate_non_negative_index("v1", "get_base1(num_non_zero,2,\"num_non_zero\",1)", get_base1(num_non_zero,2,"num_non_zero",1));
-        v1 = std::vector<int>(get_base1(num_non_zero,2,"num_non_zero",1),int(0));
-        vals_i__ = context__.vals_i("v1");
-        pos__ = 0;
-        size_t v1_limit_0__ = get_base1(num_non_zero,2,"num_non_zero",1);
-        for (size_t i_0__ = 0; i_0__ < v1_limit_0__; ++i_0__) {
-            v1[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u0", "(logical_gt(t,0) ? (get_base1(N,1,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ));
-        context__.validate_dims("data initialization", "u0", "int", context__.to_vec((logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 )));
-        validate_non_negative_index("u0", "(logical_gt(t,0) ? (get_base1(N,1,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ));
-        u0 = std::vector<int>((logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u0");
-        pos__ = 0;
-        size_t u0_limit_0__ = (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u0_limit_0__; ++i_0__) {
-            u0[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u1", "(logical_gt(t,0) ? (get_base1(N,2,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ));
-        context__.validate_dims("data initialization", "u1", "int", context__.to_vec((logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 )));
-        validate_non_negative_index("u1", "(logical_gt(t,0) ? (get_base1(N,2,\"N\",1) + 1) : 0 )", (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ));
-        u1 = std::vector<int>((logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u1");
-        pos__ = 0;
-        size_t u1_limit_0__ = (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u1_limit_0__; ++i_0__) {
-            u1[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"K",K,0);
-        for (int k0__ = 0; k0__ < 2; ++k0__) {
-            check_greater_or_equal(function__,"N[k0__]",N[k0__],1);
-        }
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X0",nnz_X0,0);
-        for (int k0__ = 0; k0__ < nnz_X0; ++k0__) {
-            check_greater_or_equal(function__,"v_X0[k0__]",v_X0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (get_base1(N,1,"N",1) + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X0[k0__]",u_X0[k0__],0);
-        }
-        check_greater_or_equal(function__,"nnz_X1",nnz_X1,0);
-        for (int k0__ = 0; k0__ < nnz_X1; ++k0__) {
-            check_greater_or_equal(function__,"v_X1[k0__]",v_X1[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (get_base1(N,2,"N",1) + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X1[k0__]",u_X1[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < 2; ++k0__) {
-            check_greater_or_equal(function__,"num_non_zero[k0__]",num_non_zero[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < get_base1(num_non_zero,1,"num_non_zero",1); ++k0__) {
-            check_greater_or_equal(function__,"v0[k0__]",v0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < get_base1(num_non_zero,2,"num_non_zero",1); ++k0__) {
-            check_greater_or_equal(function__,"v1[k0__]",v1[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (get_base1(N,1,"N",1) + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u0[k0__]",u0[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (get_base1(N,2,"N",1) + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u1[k0__]",u1[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        NN = int(0);
-        stan::math::fill(NN, std::numeric_limits<int>::min());
-        stan::math::assign(NN,(get_base1(N,1,"N",1) + get_base1(N,2,"N",1)));
-        aux = double(0);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::not_a_number());
-        validate_non_negative_index("V0", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-        V0 = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(get_base1(N,1,"N",1),int(0)));
-        stan::math::fill(V0, std::numeric_limits<int>::min());
-        stan::math::assign(V0,make_V(get_base1(N,1,"N",1),(special_case ? t : 0 ),v0, pstream__));
-        validate_non_negative_index("V1", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-        V1 = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(get_base1(N,2,"N",1),int(0)));
-        stan::math::fill(V1, std::numeric_limits<int>::min());
-        stan::math::assign(V1,make_V(get_base1(N,2,"N",1),(special_case ? t : 0 ),v1, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            current_statement_begin__ = 471;
-            if (as_bool(logical_lte(prior_dist,2))) {
-                current_statement_begin__ = 471;
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                current_statement_begin__ = 472;
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                current_statement_begin__ = 473;
-                stan::math::assign(hs, 4);
-            } else {
-                current_statement_begin__ = 474;
-                stan::math::assign(hs, 0);
-            }
-            current_statement_begin__ = 476;
-            stan::math::assign(pos, 1);
-            current_statement_begin__ = 477;
-            for (int i = 1; i <= t; ++i) {
-
-                current_statement_begin__ = 478;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    current_statement_begin__ = 479;
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        current_statement_begin__ = 480;
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        current_statement_begin__ = 481;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                current_statement_begin__ = 484;
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    current_statement_begin__ = 484;
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < get_base1(N,1,"N",1); ++k1__) {
-                check_greater_or_equal(function__,"V0[k0__][k1__]",V0[k0__][k1__],1);
-            }
-        }
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < get_base1(N,2,"N",1); ++k1__) {
-                check_greater_or_equal(function__,"V1[k0__][k1__]",V1[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-    }
-
-    ~model_bernoulli() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_ub_unconstrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),lp__));
-            else
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 512;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 512;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 513;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 514;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 515;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 518;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 519;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 520;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 523;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 524;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 525;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 528;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 530;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 533;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 534;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 535;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 536;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 537;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 538;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 540;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 544;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 545;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 546;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 546;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 547;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 549;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 550;
-                if (as_bool(special_case)) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 552;
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        current_statement_begin__ = 553;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 553;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 554;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 556;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 557;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 561;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 563;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) eta0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta0, DUMMY_VAR__);
-                stan::math::fill(eta0,DUMMY_VAR__);
-                validate_non_negative_index("eta1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) eta1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta1, DUMMY_VAR__);
-                stan::math::fill(eta1,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 571;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 572;
-                    if (as_bool(dense_X)) {
-
-                        current_statement_begin__ = 573;
-                        stan::math::assign(eta0, multiply(get_base1(X0,1,"X0",1),beta));
-                        current_statement_begin__ = 574;
-                        stan::math::assign(eta1, multiply(get_base1(X1,1,"X1",1),beta));
-                    } else {
-
-                        current_statement_begin__ = 577;
-                        stan::math::assign(eta0, csr_matrix_times_vector(get_base1(N,1,"N",1),K,w_X0,v_X0,u_X0,beta));
-                        current_statement_begin__ = 578;
-                        stan::math::assign(eta1, csr_matrix_times_vector(get_base1(N,2,"N",1),K,w_X1,v_X1,u_X1,beta));
-                    }
-                } else {
-
-                    current_statement_begin__ = 582;
-                    stan::math::assign(eta0, rep_vector(0.0,get_base1(N,1,"N",1)));
-                    current_statement_begin__ = 583;
-                    stan::math::assign(eta1, rep_vector(0.0,get_base1(N,2,"N",1)));
-                }
-                current_statement_begin__ = 585;
-                if (as_bool((primitive_value(logical_eq(has_intercept,0)) && primitive_value(dense_X)))) {
-                    {
-                        T__ tmp;
-                        (void) tmp;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(tmp, DUMMY_VAR__);
-                        stan::math::fill(tmp,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 587;
-                        stan::math::assign(tmp, dot_product(xbar,beta));
-                        current_statement_begin__ = 588;
-                        stan::math::assign(eta0, add(eta0,tmp));
-                        current_statement_begin__ = 589;
-                        stan::math::assign(eta1, add(eta1,tmp));
-                    }
-                }
-                current_statement_begin__ = 591;
-                if (as_bool(logical_eq(has_offset,1))) {
-
-                    current_statement_begin__ = 592;
-                    stan::math::assign(eta0, add(eta0,offset0));
-                    current_statement_begin__ = 593;
-                    stan::math::assign(eta1, add(eta1,offset1));
-                }
-                current_statement_begin__ = 595;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 596;
-                    stan::math::assign(eta0, add(eta0,multiply(S0,beta_smooth)));
-                    current_statement_begin__ = 597;
-                    stan::math::assign(eta1, add(eta1,multiply(S1,beta_smooth)));
-                }
-                current_statement_begin__ = 599;
-                if (as_bool(special_case)) {
-                    current_statement_begin__ = 599;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 600;
-                        stan::math::assign(eta0, add(eta0,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V0,i,"V0",1)), stan::model::nil_index_list()), "b")));
-                        current_statement_begin__ = 601;
-                        stan::math::assign(eta1, add(eta1,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V1,i,"V1",1)), stan::model::nil_index_list()), "b")));
-                    }
-                } else if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 604;
-                    stan::math::assign(eta0, add(eta0,csr_matrix_times_vector(get_base1(N,1,"N",1),q,w0,v0,u0,b)));
-                    current_statement_begin__ = 605;
-                    stan::math::assign(eta1, add(eta1,csr_matrix_times_vector(get_base1(N,2,"N",1),q,w1,v1,u1,b)));
-                }
-                current_statement_begin__ = 607;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 608;
-                    if (as_bool(logical_neq(link,4))) {
-
-                        current_statement_begin__ = 609;
-                        stan::math::assign(eta0, add(get_base1(gamma,1,"gamma",1),eta0));
-                        current_statement_begin__ = 610;
-                        stan::math::assign(eta1, add(get_base1(gamma,1,"gamma",1),eta1));
-                    } else {
-                        {
-                            T__ shift;
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, DUMMY_VAR__);
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 614;
-                            stan::math::assign(shift, stan::math::fmax(max(eta0),max(eta1)));
-                            current_statement_begin__ = 615;
-                            stan::math::assign(eta0, subtract(add(get_base1(gamma,1,"gamma",1),eta0),shift));
-                            current_statement_begin__ = 616;
-                            stan::math::assign(eta1, subtract(add(get_base1(gamma,1,"gamma",1),eta1),shift));
-                        }
-                    }
-                }
-                current_statement_begin__ = 620;
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 622;
-                        stan::math::assign(dummy, ll_bern_lp(eta0,eta1,link,N, lp__, lp_accum__, pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-
-                    current_statement_begin__ = 625;
-                    lp_accum__.add(dot_product(weights0,pw_bern(0,eta0,link, pstream__)));
-                    current_statement_begin__ = 626;
-                    lp_accum__.add(dot_product(weights1,pw_bern(1,eta1,link, pstream__)));
-                }
-                current_statement_begin__ = 632;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 632;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    current_statement_begin__ = 633;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 636;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 637;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 638;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 639;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 640;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 644;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 645;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 646;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 647;
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 649;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        current_statement_begin__ = 650;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 651;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    current_statement_begin__ = 654;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 655;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    current_statement_begin__ = 658;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 659;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    current_statement_begin__ = 660;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    current_statement_begin__ = 663;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                current_statement_begin__ = 668;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 669;
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        current_statement_begin__ = 670;
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        current_statement_begin__ = 672;
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                current_statement_begin__ = 677;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 678;
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    current_statement_begin__ = 679;
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            current_statement_begin__ = 681;
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                current_statement_begin__ = 682;
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                current_statement_begin__ = 684;
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                current_statement_begin__ = 686;
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 689;
-                if (as_bool(logical_gt(t,0))) {
-                    current_statement_begin__ = 689;
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-                current_statement_begin__ = 690;
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_bernoulli_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 512;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 512;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 513;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 514;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 515;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 518;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 519;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 520;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 523;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 524;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 525;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 528;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 530;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 533;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 534;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 535;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 536;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 537;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 538;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 540;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 544;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 545;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 546;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 546;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 547;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 549;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 550;
-                if (as_bool(special_case)) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 552;
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        current_statement_begin__ = 553;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 553;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 554;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 556;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 557;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 561;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 563;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            current_statement_begin__ = 695;
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                current_statement_begin__ = 696;
-                if (as_bool(dense_X)) {
-                    current_statement_begin__ = 696;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    current_statement_begin__ = 697;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("pi0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                vector_d pi0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) pi0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi0, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi0,DUMMY_VAR__);
-                validate_non_negative_index("pi1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                vector_d pi1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) pi1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi1, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi1,DUMMY_VAR__);
-                validate_non_negative_index("eta0", "get_base1(N,1,\"N\",1)", get_base1(N,1,"N",1));
-                vector_d eta0(static_cast<Eigen::VectorXd::Index>(get_base1(N,1,"N",1)));
-                (void) eta0;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta0, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta0,DUMMY_VAR__);
-                validate_non_negative_index("eta1", "get_base1(N,2,\"N\",1)", get_base1(N,2,"N",1));
-                vector_d eta1(static_cast<Eigen::VectorXd::Index>(get_base1(N,2,"N",1)));
-                (void) eta1;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta1, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta1,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 705;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 706;
-                    if (as_bool(dense_X)) {
-
-                        current_statement_begin__ = 707;
-                        stan::math::assign(eta0, multiply(get_base1(X0,1,"X0",1),beta));
-                        current_statement_begin__ = 708;
-                        stan::math::assign(eta1, multiply(get_base1(X1,1,"X1",1),beta));
-                    } else {
-
-                        current_statement_begin__ = 711;
-                        stan::math::assign(eta0, csr_matrix_times_vector(get_base1(N,1,"N",1),K,w_X0,v_X0,u_X0,beta));
-                        current_statement_begin__ = 712;
-                        stan::math::assign(eta1, csr_matrix_times_vector(get_base1(N,2,"N",1),K,w_X1,v_X1,u_X1,beta));
-                    }
-                } else {
-
-                    current_statement_begin__ = 716;
-                    stan::math::assign(eta0, rep_vector(0.0,get_base1(N,1,"N",1)));
-                    current_statement_begin__ = 717;
-                    stan::math::assign(eta1, rep_vector(0.0,get_base1(N,2,"N",1)));
-                }
-                current_statement_begin__ = 719;
-                if (as_bool((primitive_value(logical_eq(has_intercept,0)) && primitive_value(dense_X)))) {
-                    {
-                        double tmp(0.0);
-                        (void) tmp;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(tmp, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(tmp,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 721;
-                        stan::math::assign(tmp, dot_product(xbar,beta));
-                        current_statement_begin__ = 722;
-                        stan::math::assign(eta0, add(eta0,tmp));
-                        current_statement_begin__ = 723;
-                        stan::math::assign(eta1, add(eta1,tmp));
-                    }
-                }
-                current_statement_begin__ = 725;
-                if (as_bool(logical_eq(has_offset,1))) {
-
-                    current_statement_begin__ = 726;
-                    stan::math::assign(eta0, add(eta0,offset0));
-                    current_statement_begin__ = 727;
-                    stan::math::assign(eta1, add(eta1,offset1));
-                }
-                current_statement_begin__ = 729;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 730;
-                    stan::math::assign(eta0, add(eta0,multiply(S0,beta_smooth)));
-                    current_statement_begin__ = 731;
-                    stan::math::assign(eta1, add(eta1,multiply(S1,beta_smooth)));
-                }
-                current_statement_begin__ = 733;
-                if (as_bool(special_case)) {
-                    current_statement_begin__ = 733;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 734;
-                        stan::math::assign(eta0, add(eta0,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V0,i,"V0",1)), stan::model::nil_index_list()), "b")));
-                        current_statement_begin__ = 735;
-                        stan::math::assign(eta1, add(eta1,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V1,i,"V1",1)), stan::model::nil_index_list()), "b")));
-                    }
-                } else if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 738;
-                    stan::math::assign(eta0, add(eta0,csr_matrix_times_vector(get_base1(N,1,"N",1),q,w0,v0,u0,b)));
-                    current_statement_begin__ = 739;
-                    stan::math::assign(eta1, add(eta1,csr_matrix_times_vector(get_base1(N,2,"N",1),q,w1,v1,u1,b)));
-                }
-                current_statement_begin__ = 741;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 742;
-                    if (as_bool(logical_neq(link,4))) {
-
-                        current_statement_begin__ = 743;
-                        stan::math::assign(eta0, add(get_base1(gamma,1,"gamma",1),eta0));
-                        current_statement_begin__ = 744;
-                        stan::math::assign(eta1, add(get_base1(gamma,1,"gamma",1),eta1));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 748;
-                            stan::math::assign(shift, stan::math::fmax(max(eta0),max(eta1)));
-                            current_statement_begin__ = 749;
-                            stan::math::assign(eta0, subtract(add(get_base1(gamma,1,"gamma",1),eta0),shift));
-                            current_statement_begin__ = 750;
-                            stan::math::assign(eta1, subtract(add(get_base1(gamma,1,"gamma",1),eta1),shift));
-                            current_statement_begin__ = 751;
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                }
-                current_statement_begin__ = 754;
-                stan::math::assign(pi0, linkinv_bern(eta0,link, pstream__));
-                current_statement_begin__ = 755;
-                stan::math::assign(pi1, linkinv_bern(eta1,link, pstream__));
-                current_statement_begin__ = 756;
-                for (int n = 1; n <= get_base1(N,1,"N",1); ++n) {
-                    current_statement_begin__ = 756;
-                    stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(get_base1(pi0,n,"pi0",1), base_rng__)));
-                }
-                current_statement_begin__ = 757;
-                for (int n = 1; n <= get_base1(N,2,"N",1); ++n) {
-                    current_statement_begin__ = 757;
-                    stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(get_base1(pi1,n,"pi1",1), base_rng__)));
-                }
-                current_statement_begin__ = 758;
-                stan::math::assign(mean_PPD, (mean_PPD / NN));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_bernoulli";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_binomial_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_binomial");
-    reader.add_event(690, 690, "end", "model_binomial");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 52;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 54;
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        current_statement_begin__ = 55;
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        current_statement_begin__ = 57;
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            current_statement_begin__ = 65;
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            current_statement_begin__ = 68;
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            current_statement_begin__ = 69;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 70;
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            current_statement_begin__ = 73;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 74;
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            current_statement_begin__ = 75;
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            current_statement_begin__ = 76;
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            current_statement_begin__ = 77;
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            current_statement_begin__ = 79;
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    current_statement_begin__ = 83;
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    current_statement_begin__ = 84;
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    current_statement_begin__ = 85;
-                                    for (int c = 1; c <= r; ++c) {
-                                        current_statement_begin__ = 85;
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    current_statement_begin__ = 86;
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    current_statement_begin__ = 87;
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 91;
-                            for (int c = 1; c <= nc; ++c) {
-                                current_statement_begin__ = 91;
-                                for (int r = c; r <= nc; ++r) {
-
-                                    current_statement_begin__ = 92;
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    current_statement_begin__ = 93;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 97;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 115;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 117;
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            current_statement_begin__ = 119;
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                current_statement_begin__ = 120;
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            current_statement_begin__ = 121;
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            current_statement_begin__ = 122;
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            current_statement_begin__ = 126;
-                            for (int c = 1; c <= nc; ++c) {
-
-                                current_statement_begin__ = 127;
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                current_statement_begin__ = 128;
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                current_statement_begin__ = 129;
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    current_statement_begin__ = 130;
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    current_statement_begin__ = 131;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 134;
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    current_statement_begin__ = 136;
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    current_statement_begin__ = 137;
-                                    for (int s = 1; s <= nc; ++s) {
-                                        current_statement_begin__ = 137;
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    current_statement_begin__ = 138;
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 142;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            current_statement_begin__ = 165;
-            lp_accum__.add(normal_log(z_b,0,1));
-            current_statement_begin__ = 166;
-            lp_accum__.add(normal_log(z_T,0,1));
-            current_statement_begin__ = 167;
-            for (int i = 1; i <= t; ++i) {
-                current_statement_begin__ = 167;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        current_statement_begin__ = 171;
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        current_statement_begin__ = 172;
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        current_statement_begin__ = 173;
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        current_statement_begin__ = 174;
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            current_statement_begin__ = 175;
-                            stan::math::assign(nu, (nu - 0.5));
-                            current_statement_begin__ = 176;
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            current_statement_begin__ = 177;
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        current_statement_begin__ = 179;
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        current_statement_begin__ = 180;
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 182;
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            current_statement_begin__ = 183;
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            current_statement_begin__ = 200;
-            stan::math::assign(K, rows(z_beta));
-            current_statement_begin__ = 201;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 201;
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            current_statement_begin__ = 202;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 218;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 233;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 233;
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            current_statement_begin__ = 234;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            current_statement_begin__ = 256;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 271;
-            if (as_bool(logical_gt(t,0))) {
-                current_statement_begin__ = 271;
-                for (int j = 1; j <= N; ++j) {
-                    current_statement_begin__ = 271;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 272;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        current_statement_begin__ = 273;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 275;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_binom(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 287;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 287;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 288;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 289;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 290;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 291;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            current_statement_begin__ = 292;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 292;
-        current_statement_begin__ = 293;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_binom_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_binom(eta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T2__, T_lp__>::type
-ll_binom_lp(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 305;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 305;
-            lp_accum__.add(binomial_logit_log(y,trials,eta));
-        } else if (as_bool(logical_lt(link,4))) {
-            current_statement_begin__ = 306;
-            lp_accum__.add(binomial_log(y,trials,linkinv_binom(eta,link, pstream__)));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            current_statement_begin__ = 308;
-            for (int n = 1; n <= num_elements(y); ++n) {
-
-                current_statement_begin__ = 309;
-                lp_accum__.add((get_base1(y,n,"y",1) * get_base1(eta,n,"eta",1)));
-                current_statement_begin__ = 310;
-                lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * log1m_exp(get_base1(eta,n,"eta",1))));
-                current_statement_begin__ = 311;
-                lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-            }
-        } else if (as_bool(logical_eq(link,5))) {
-
-            current_statement_begin__ = 315;
-            for (int n = 1; n <= num_elements(y); ++n) {
-                {
-                    fun_scalar_t__ neg_exp_eta;
-                    (void) neg_exp_eta;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(neg_exp_eta, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(neg_exp_eta,DUMMY_VAR__);
-                    stan::math::assign(neg_exp_eta,-(exp(get_base1(eta,n,"eta",1))));
-
-
-                    current_statement_begin__ = 317;
-                    lp_accum__.add((get_base1(y,n,"y",1) * log1m_exp(neg_exp_eta)));
-                    current_statement_begin__ = 318;
-                    lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * neg_exp_eta));
-                    current_statement_begin__ = 319;
-                    lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-                }
-            }
-        } else {
-            current_statement_begin__ = 322;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 322;
-        current_statement_begin__ = 323;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_binom_lp_functor__ {
-    template <typename T2__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T2__, T_lp__>::type
-    operator()(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_binom_lp(y, trials, eta, link, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-pw_binom(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 336;
-            if (as_bool(logical_eq(link,1))) {
-
-                current_statement_begin__ = 337;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 338;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_logit_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_binom(eta,link, pstream__));
-
-
-                    current_statement_begin__ = 342;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 342;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 344;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 344;
-            current_statement_begin__ = 345;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_binom_functor__ {
-    template <typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) const {
-        return pw_binom(y, trials, eta, link, pstream__);
-    }
-};
-
-class model_binomial : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    vector<int> y;
-    vector<int> trials;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    double aux;
-    vector<vector<int> > V;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_binomial(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_binomial(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_binomial_namespace::model_binomial";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("trials", "N", N);
-        context__.validate_dims("data initialization", "trials", "int", context__.to_vec(N));
-        validate_non_negative_index("trials", "N", N);
-        trials = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("trials");
-        pos__ = 0;
-        size_t trials_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < trials_limit_0__; ++i_0__) {
-            trials[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"trials[k0__]",trials[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        aux = double(0);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::not_a_number());
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "N", N);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(N,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(N,(special_case ? t : 0 ),v, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            current_statement_begin__ = 455;
-            if (as_bool(logical_lte(prior_dist,2))) {
-                current_statement_begin__ = 455;
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                current_statement_begin__ = 456;
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                current_statement_begin__ = 457;
-                stan::math::assign(hs, 4);
-            } else {
-                current_statement_begin__ = 458;
-                stan::math::assign(hs, 0);
-            }
-            current_statement_begin__ = 460;
-            stan::math::assign(pos, 1);
-            current_statement_begin__ = 461;
-            for (int i = 1; i <= t; ++i) {
-
-                current_statement_begin__ = 462;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    current_statement_begin__ = 463;
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        current_statement_begin__ = 464;
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        current_statement_begin__ = 465;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                current_statement_begin__ = 468;
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    current_statement_begin__ = 468;
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < N; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-    }
-
-    ~model_binomial() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_ub_unconstrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() ),lp__));
-            else
-                gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 496;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 496;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 497;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 498;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 499;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 502;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 503;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 504;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 507;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 508;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 509;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 512;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 514;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 517;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 518;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 519;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 520;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 521;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 522;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 524;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 528;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 529;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 530;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 530;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 531;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 533;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 534;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 536;
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        current_statement_begin__ = 537;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 537;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 538;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 540;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 541;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 545;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 547;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 554;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 555;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 555;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 556;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 558;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 559;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 559;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 560;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 560;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 561;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 563;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 563;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 563;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 564;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 566;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 567;
-                    if (as_bool(logical_neq(link,4))) {
-                        current_statement_begin__ = 567;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        current_statement_begin__ = 568;
-                        stan::math::assign(eta, subtract(add(get_base1(gamma,1,"gamma",1),eta),max(eta)));
-                    }
-                } else {
-
-                    current_statement_begin__ = 573;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 577;
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 579;
-                        stan::math::assign(dummy, ll_binom_lp(y,trials,eta,link, lp__, lp_accum__, pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    current_statement_begin__ = 582;
-                    lp_accum__.add(dot_product(weights,pw_binom(y,trials,eta,link, pstream__)));
-                }
-                current_statement_begin__ = 587;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 587;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    current_statement_begin__ = 588;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 591;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 592;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 593;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 594;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 595;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 599;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 600;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 601;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 602;
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 604;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        current_statement_begin__ = 605;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 606;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    current_statement_begin__ = 609;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 610;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    current_statement_begin__ = 613;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 614;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    current_statement_begin__ = 615;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    current_statement_begin__ = 618;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                current_statement_begin__ = 623;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 624;
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        current_statement_begin__ = 625;
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        current_statement_begin__ = 627;
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                current_statement_begin__ = 632;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 633;
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    current_statement_begin__ = 634;
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            current_statement_begin__ = 636;
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                current_statement_begin__ = 637;
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                current_statement_begin__ = 639;
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                current_statement_begin__ = 641;
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 645;
-                if (as_bool(logical_gt(t,0))) {
-                    current_statement_begin__ = 645;
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-                current_statement_begin__ = 646;
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_binomial_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_ub_constrain((logical_eq(link,4) ? 0.0 : stan::math::positive_infinity() )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 496;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 496;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 497;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 498;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 499;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 502;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 503;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 504;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 507;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 508;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 509;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 512;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 514;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 517;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 518;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 519;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 520;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 521;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 522;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 524;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 528;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 529;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 530;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 530;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 531;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 533;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 534;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 536;
-                        stan::math::assign(theta_L, elt_multiply(scale,tau));
-                        current_statement_begin__ = 537;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 537;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 538;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 540;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 541;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 545;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 547;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            current_statement_begin__ = 651;
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                current_statement_begin__ = 652;
-                if (as_bool(dense_X)) {
-                    current_statement_begin__ = 652;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    current_statement_begin__ = 653;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("pi", "N", N);
-                vector_d pi(static_cast<Eigen::VectorXd::Index>(N));
-                (void) pi;  // dummy to suppress unused var warning
-
-                stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(pi,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 659;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 660;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 660;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 661;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 663;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 664;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 664;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 665;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 665;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 666;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 668;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 668;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 668;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 669;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 671;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 672;
-                    if (as_bool(logical_neq(link,4))) {
-                        current_statement_begin__ = 672;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 675;
-                            stan::math::assign(shift, max(eta));
-                            current_statement_begin__ = 676;
-                            stan::math::assign(eta, subtract(add(get_base1(gamma,1,"gamma",1),eta),shift));
-                            current_statement_begin__ = 677;
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 683;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 686;
-                stan::math::assign(pi, linkinv_binom(eta,link, pstream__));
-                current_statement_begin__ = 687;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 687;
-                    stan::math::assign(mean_PPD, (mean_PPD + binomial_rng(get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1), base_rng__)));
-                }
-                current_statement_begin__ = 688;
-                stan::math::assign(mean_PPD, (mean_PPD / N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_binomial";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_continuous_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_continuous");
-    reader.add_event(1369, 1369, "end", "model_continuous");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 52;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 54;
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        current_statement_begin__ = 55;
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        current_statement_begin__ = 57;
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            current_statement_begin__ = 65;
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            current_statement_begin__ = 68;
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            current_statement_begin__ = 69;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 70;
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            current_statement_begin__ = 73;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 74;
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            current_statement_begin__ = 75;
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            current_statement_begin__ = 76;
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            current_statement_begin__ = 77;
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            current_statement_begin__ = 79;
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    current_statement_begin__ = 83;
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    current_statement_begin__ = 84;
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    current_statement_begin__ = 85;
-                                    for (int c = 1; c <= r; ++c) {
-                                        current_statement_begin__ = 85;
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    current_statement_begin__ = 86;
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    current_statement_begin__ = 87;
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 91;
-                            for (int c = 1; c <= nc; ++c) {
-                                current_statement_begin__ = 91;
-                                for (int r = c; r <= nc; ++r) {
-
-                                    current_statement_begin__ = 92;
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    current_statement_begin__ = 93;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 97;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 115;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 117;
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            current_statement_begin__ = 119;
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                current_statement_begin__ = 120;
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            current_statement_begin__ = 121;
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            current_statement_begin__ = 122;
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            current_statement_begin__ = 126;
-                            for (int c = 1; c <= nc; ++c) {
-
-                                current_statement_begin__ = 127;
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                current_statement_begin__ = 128;
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                current_statement_begin__ = 129;
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    current_statement_begin__ = 130;
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    current_statement_begin__ = 131;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 134;
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    current_statement_begin__ = 136;
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    current_statement_begin__ = 137;
-                                    for (int s = 1; s <= nc; ++s) {
-                                        current_statement_begin__ = 137;
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    current_statement_begin__ = 138;
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 142;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            current_statement_begin__ = 165;
-            lp_accum__.add(normal_log(z_b,0,1));
-            current_statement_begin__ = 166;
-            lp_accum__.add(normal_log(z_T,0,1));
-            current_statement_begin__ = 167;
-            for (int i = 1; i <= t; ++i) {
-                current_statement_begin__ = 167;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        current_statement_begin__ = 171;
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        current_statement_begin__ = 172;
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        current_statement_begin__ = 173;
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        current_statement_begin__ = 174;
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            current_statement_begin__ = 175;
-                            stan::math::assign(nu, (nu - 0.5));
-                            current_statement_begin__ = 176;
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            current_statement_begin__ = 177;
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        current_statement_begin__ = 179;
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        current_statement_begin__ = 180;
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 182;
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            current_statement_begin__ = 183;
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            current_statement_begin__ = 200;
-            stan::math::assign(K, rows(z_beta));
-            current_statement_begin__ = 201;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 201;
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            current_statement_begin__ = 202;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 218;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 233;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 233;
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            current_statement_begin__ = 234;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            current_statement_begin__ = 256;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 271;
-            if (as_bool(logical_gt(t,0))) {
-                current_statement_begin__ = 271;
-                for (int j = 1; j <= N; ++j) {
-                    current_statement_begin__ = 271;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 272;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        current_statement_begin__ = 273;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 275;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 287;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 287;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 288;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 289;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            current_statement_begin__ = 290;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 290;
-        current_statement_begin__ = 291;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gauss_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gauss(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 302;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(subtract((-(0.5) * log((6.2831853071795862 * sigma))),multiply(0.5,square(divide(subtract(y,linkinv_gauss(eta,link, pstream__)),sigma)))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gauss_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gauss(y, eta, sigma, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 314;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 314;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 315;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 316;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            current_statement_begin__ = 317;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 317;
-        current_statement_begin__ = 318;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gamma_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gamma(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-GammaReg(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ ret;
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-            stan::math::assign(ret,((rows(y) * ((shape * log(shape)) - stan::math::lgamma(shape))) + ((shape - 1) * sum_log_y)));
-
-
-            current_statement_begin__ = 335;
-            if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 336;
-                stan::math::assign(ret, ((ret - (shape * sum(eta))) - (shape * sum(elt_divide(y,exp(eta))))));
-            } else if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 338;
-                stan::math::assign(ret, ((ret - (shape * sum(log(eta)))) - (shape * sum(elt_divide(y,eta)))));
-            } else if (as_bool(logical_eq(link,3))) {
-                current_statement_begin__ = 340;
-                stan::math::assign(ret, ((ret + (shape * sum(log(eta)))) - (shape * dot_product(eta,y))));
-            } else {
-                current_statement_begin__ = 341;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 341;
-            current_statement_begin__ = 342;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct GammaReg_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) const {
-        return GammaReg(y, eta, shape, link, sum_log_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 356;
-            if (as_bool(logical_eq(link,3))) {
-
-                current_statement_begin__ = 357;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 358;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape * get_base1(eta,n,"eta",1))));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-
-                current_statement_begin__ = 362;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 363;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / exp(get_base1(eta,n,"eta",1)))));
-                }
-            } else if (as_bool(logical_eq(link,1))) {
-
-                current_statement_begin__ = 367;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 368;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / get_base1(eta,n,"eta",1))));
-                }
-            } else {
-                current_statement_begin__ = 371;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 371;
-            current_statement_begin__ = 372;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gamma_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gamma(y, eta, shape, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 383;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 383;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 384;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 385;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 386;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_sqrt(eta));
-        } else {
-            current_statement_begin__ = 387;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 387;
-        current_statement_begin__ = 388;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_inv_gaussian_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) const {
-        return linkinv_inv_gaussian(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 403;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(((((0.5 * rows(y)) * log((lambda / 6.2831853071795862))) - (1.5 * sum_log_y)) - ((0.5 * lambda) * dot_self(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y))))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return inv_gaussian(y, mu, lambda, sum_log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-pw_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_inv_gaussian(eta,link, pstream__));
-
-
-            current_statement_begin__ = 422;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(add(multiply((-(0.5) * lambda),square(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y)))),(0.5 * log((lambda / 6.2831853071795862)))),multiply(1.5,log_y)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return pw_inv_gaussian(y, eta, lambda, link, log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-inv_gaussian_rng(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ mu2;
-            (void) mu2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu2,DUMMY_VAR__);
-            stan::math::assign(mu2,square(mu));
-            fun_scalar_t__ z;
-            (void) z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z,DUMMY_VAR__);
-            stan::math::assign(z,uniform_rng(0,1, base_rng__));
-            fun_scalar_t__ y;
-            (void) y;  // dummy to suppress unused var warning
-
-            stan::math::initialize(y, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(y,DUMMY_VAR__);
-            stan::math::assign(y,square(normal_rng(0,1, base_rng__)));
-            fun_scalar_t__ x;
-            (void) x;  // dummy to suppress unused var warning
-
-            stan::math::initialize(x, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(x,DUMMY_VAR__);
-            stan::math::assign(x,(mu + (((mu2 * y) - (mu * sqrt(((((4 * mu) * lambda) * y) + (mu2 * square(y)))))) / (2 * lambda))));
-
-
-            current_statement_begin__ = 441;
-            if (as_bool(logical_lte(z,(mu / (mu + x))))) {
-                current_statement_begin__ = 441;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-            } else {
-                current_statement_begin__ = 442;
-                return stan::math::promote_scalar<fun_return_scalar_t__>((mu2 / x));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_rng_functor__ {
-    template <typename T0__, typename T1__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) const {
-        return inv_gaussian_rng(mu, lambda, base_rng__, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 453;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 453;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 454;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 455;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 456;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(0.5,divide(atan(eta),stan::math::pi())));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 457;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,6))) {
-            current_statement_begin__ = 458;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(1,inv_cloglog(minus(eta))));
-        } else {
-            current_statement_begin__ = 459;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 459;
-        current_statement_begin__ = 460;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_beta(eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 471;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 471;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 472;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 473;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            current_statement_begin__ = 474;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 475;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_z_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) const {
-        return linkinv_beta_z(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-
-
-            current_statement_begin__ = 490;
-            for (int n = 1; n <= rows(y); ++n) {
-
-                current_statement_begin__ = 491;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * dispersion),((1 - get_base1(mu,n,"mu",1)) * dispersion)));
-            }
-            current_statement_begin__ = 493;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) const {
-        return pw_beta(y, eta, dispersion, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-            validate_non_negative_index("mu_z", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu_z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu_z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu_z,DUMMY_VAR__);
-            stan::math::assign(mu_z,linkinv_beta_z(eta_z,link_phi, pstream__));
-
-
-            current_statement_begin__ = 510;
-            for (int n = 1; n <= rows(y); ++n) {
-
-                current_statement_begin__ = 511;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * get_base1(mu_z,n,"mu_z",1)),((1 - get_base1(mu,n,"mu",1)) * get_base1(mu_z,n,"mu_z",1))));
-            }
-            current_statement_begin__ = 513;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_z_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) const {
-        return pw_beta_z(y, eta, eta_z, link, link_phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asymp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOff(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOrig(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_biexp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-SS_fol(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_fpl(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_gompertz(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_logis(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_micmen(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_weibull(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__);
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asymp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 535;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            {
-                validate_non_negative_index("Asym", "rows(Phi)", rows(Phi));
-                Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  Asym(static_cast<Eigen::VectorXd::Index>(rows(Phi)));
-                (void) Asym;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Asym,DUMMY_VAR__);
-                stan::math::assign(Asym,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-
-
-                current_statement_begin__ = 537;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(Asym,elt_multiply(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),Asym),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"))),input)))));
-            }
-        } else {
-            {
-                fun_scalar_t__ Asym;
-                (void) Asym;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Asym,DUMMY_VAR__);
-                stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-
-
-                current_statement_begin__ = 541;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(Asym,multiply((get_base1(Phi,1,2,"Phi",1) - Asym),exp(multiply(-(exp(get_base1(Phi,1,3,"Phi",1))),input)))));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asymp_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asymp(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOff(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 547;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            current_statement_begin__ = 548;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),subtract(1,exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),subtract(input,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))))));
-        } else {
-            current_statement_begin__ = 550;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(get_base1(Phi,1,1,"Phi",1),subtract(1,exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),subtract(input,get_base1(Phi,1,3,"Phi",1)))))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asympOff_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asympOff(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_asympOrig(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 555;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            current_statement_begin__ = 556;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),subtract(1,exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),input)))));
-        } else {
-            current_statement_begin__ = 558;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(get_base1(Phi,1,1,"Phi",1),subtract(1,exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),input)))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_asympOrig_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_asympOrig(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_biexp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 563;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            current_statement_begin__ = 564;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"))),input))),elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"),exp(elt_multiply(minus(exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(4), stan::model::nil_index_list())), "Phi"))),input)))));
-        } else {
-            current_statement_begin__ = 567;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(multiply(get_base1(Phi,1,1,"Phi",1),exp(multiply(-(exp(get_base1(Phi,1,2,"Phi",1))),input))),multiply(get_base1(Phi,1,3,"Phi",1),exp(multiply(-(exp(get_base1(Phi,1,4,"Phi",1))),input)))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_biexp_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_biexp(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-SS_fol(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int Phi_rows(0);
-            (void) Phi_rows;  // dummy to suppress unused var warning
-
-            stan::math::fill(Phi_rows, std::numeric_limits<int>::min());
-            stan::math::assign(Phi_rows,rows(Phi));
-
-
-            current_statement_begin__ = 574;
-            if (as_bool(logical_gt(Phi_rows,1))) {
-                {
-                    validate_non_negative_index("lKe", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lKe(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKe,DUMMY_VAR__);
-                    stan::math::assign(lKe,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-                    validate_non_negative_index("lKa", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lKa(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKa,DUMMY_VAR__);
-                    stan::math::assign(lKa,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"));
-                    validate_non_negative_index("exp_lKe", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  exp_lKe(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) exp_lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKe,DUMMY_VAR__);
-                    stan::math::assign(exp_lKe,exp(lKe));
-                    validate_non_negative_index("exp_lKa", "Phi_rows", Phi_rows);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  exp_lKa(static_cast<Eigen::VectorXd::Index>(Phi_rows));
-                    (void) exp_lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKa,DUMMY_VAR__);
-                    stan::math::assign(exp_lKa,exp(lKa));
-
-
-                    current_statement_begin__ = 579;
-                    return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(elt_multiply(elt_multiply(Dose,exp(subtract(add(lKe,lKa),stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))),subtract(exp(elt_multiply(minus(exp_lKe),input)),exp(elt_multiply(minus(exp_lKa),input)))),subtract(exp_lKa,exp_lKe)));
-                }
-            } else {
-                {
-                    fun_scalar_t__ lKe;
-                    (void) lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKe,DUMMY_VAR__);
-                    stan::math::assign(lKe,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ lKa;
-                    (void) lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lKa,DUMMY_VAR__);
-                    stan::math::assign(lKa,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ exp_lKe;
-                    (void) exp_lKe;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKe, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKe,DUMMY_VAR__);
-                    stan::math::assign(exp_lKe,exp(lKe));
-                    fun_scalar_t__ exp_lKa;
-                    (void) exp_lKa;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(exp_lKa, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(exp_lKa,DUMMY_VAR__);
-                    stan::math::assign(exp_lKa,exp(lKa));
-
-
-                    current_statement_begin__ = 587;
-                    return stan::math::promote_scalar<fun_return_scalar_t__>(divide(elt_multiply(multiply(Dose,exp(((lKe + lKa) - get_base1(Phi,1,3,"Phi",1)))),subtract(exp(multiply(-(exp_lKe),input)),exp(multiply(-(exp_lKa),input)))),(exp_lKa - exp_lKe)));
-                }
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_fol_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& Dose,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T2__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_fol(Dose, input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_fpl(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 595;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            {
-                validate_non_negative_index("A", "rows(Phi)", rows(Phi));
-                Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  A(static_cast<Eigen::VectorXd::Index>(rows(Phi)));
-                (void) A;  // dummy to suppress unused var warning
-
-                stan::math::initialize(A, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(A,DUMMY_VAR__);
-                stan::math::assign(A,stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"));
-
-
-                current_statement_begin__ = 597;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(A,elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),A),add(1,exp(elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi"),input),exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(4), stan::model::nil_index_list())), "Phi"))))))));
-            }
-        } else {
-            {
-                fun_scalar_t__ A;
-                (void) A;  // dummy to suppress unused var warning
-
-                stan::math::initialize(A, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(A,DUMMY_VAR__);
-                stan::math::assign(A,get_base1(Phi,1,1,"Phi",1));
-
-
-                current_statement_begin__ = 601;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(add(A,elt_divide(rep_vector((get_base1(Phi,1,2,"Phi",1) - A),rows(input)),add(1,exp(divide(subtract(get_base1(Phi,1,3,"Phi",1),input),exp(get_base1(Phi,1,4,"Phi",1))))))));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_fpl_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_fpl(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_gompertz(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "rows(x)", rows(x));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  out(static_cast<Eigen::VectorXd::Index>(rows(x)));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 609;
-            if (as_bool(logical_gt(rows(Phi),1))) {
-                current_statement_begin__ = 609;
-                for (int i = 1; i <= rows(x); ++i) {
-                    current_statement_begin__ = 610;
-                    stan::math::assign(get_base1_lhs(out,i,"out",1), (get_base1(Phi,i,1,"Phi",1) * exp((-(get_base1(Phi,i,2,"Phi",1)) * pow(get_base1(Phi,i,3,"Phi",1),get_base1(x,i,"x",1))))));
-                }
-            } else {
-                {
-                    fun_scalar_t__ Asym;
-                    (void) Asym;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Asym,DUMMY_VAR__);
-                    stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ b2;
-                    (void) b2;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(b2, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(b2,DUMMY_VAR__);
-                    stan::math::assign(b2,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ b3;
-                    (void) b3;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(b3, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(b3,DUMMY_VAR__);
-                    stan::math::assign(b3,get_base1(Phi,1,3,"Phi",1));
-
-
-                    current_statement_begin__ = 615;
-                    for (int i = 1; i <= rows(x); ++i) {
-                        current_statement_begin__ = 615;
-                        stan::math::assign(get_base1_lhs(out,i,"out",1), (Asym * exp((-(b2) * pow(b3,get_base1(x,i,"x",1))))));
-                    }
-                }
-            }
-            current_statement_begin__ = 617;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_gompertz_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_gompertz(x, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_logis(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 623;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            current_statement_begin__ = 624;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),add(1,exp(elt_divide(subtract(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),input),exp(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(3), stan::model::nil_index_list())), "Phi")))))));
-        } else {
-            current_statement_begin__ = 626;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(rep_vector(get_base1(Phi,1,1,"Phi",1),rows(input)),add(1,exp(divide(subtract(get_base1(Phi,1,2,"Phi",1),input),exp(get_base1(Phi,1,3,"Phi",1)))))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_logis_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_logis(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_micmen(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 632;
-        if (as_bool(logical_gt(rows(Phi),1))) {
-            current_statement_begin__ = 633;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(elt_multiply(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "Phi"),input),add(stan::model::rvalue(Phi, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "Phi"),input)));
-        } else {
-            current_statement_begin__ = 635;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(elt_divide(multiply(get_base1(Phi,1,1,"Phi",1),input),add(get_base1(Phi,1,2,"Phi",1),input)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_micmen_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& input,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_micmen(input, Phi, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-SS_weibull(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "rows(x)", rows(x));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  out(static_cast<Eigen::VectorXd::Index>(rows(x)));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 641;
-            if (as_bool(logical_gt(rows(Phi),1))) {
-                current_statement_begin__ = 641;
-                for (int i = 1; i <= rows(x); ++i) {
-                    current_statement_begin__ = 642;
-                    stan::math::assign(get_base1_lhs(out,i,"out",1), (get_base1(Phi,i,1,"Phi",1) - (get_base1(Phi,i,2,"Phi",1) * exp((-(exp(get_base1(Phi,i,3,"Phi",1))) * pow(get_base1(x,i,"x",1),get_base1(Phi,i,4,"Phi",1)))))));
-                }
-            } else {
-                {
-                    fun_scalar_t__ Asym;
-                    (void) Asym;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Asym, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Asym,DUMMY_VAR__);
-                    stan::math::assign(Asym,get_base1(Phi,1,1,"Phi",1));
-                    fun_scalar_t__ Drop;
-                    (void) Drop;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Drop, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Drop,DUMMY_VAR__);
-                    stan::math::assign(Drop,get_base1(Phi,1,2,"Phi",1));
-                    fun_scalar_t__ lrc;
-                    (void) lrc;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(lrc, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(lrc,DUMMY_VAR__);
-                    stan::math::assign(lrc,get_base1(Phi,1,3,"Phi",1));
-                    fun_scalar_t__ pwr;
-                    (void) pwr;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pwr, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pwr,DUMMY_VAR__);
-                    stan::math::assign(pwr,get_base1(Phi,1,4,"Phi",1));
-
-
-                    current_statement_begin__ = 648;
-                    for (int i = 1; i <= rows(x); ++i) {
-                        current_statement_begin__ = 649;
-                        stan::math::assign(get_base1_lhs(out,i,"out",1), (Asym - (Drop * exp((-(exp(lrc)) * pow(get_base1(x,i,"x",1),pwr))))));
-                    }
-                }
-            }
-            current_statement_begin__ = 651;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct SS_weibull_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-               const Eigen::Matrix<T1__, Eigen::Dynamic,Eigen::Dynamic>& Phi, std::ostream* pstream__) const {
-        return SS_weibull(x, Phi, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,Eigen::Dynamic>
-reshape_vec(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const int& Rows,
-                const int& Cols, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("out", "Rows", Rows);
-            validate_non_negative_index("out", "Cols", Cols);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  out(static_cast<Eigen::VectorXd::Index>(Rows),static_cast<Eigen::VectorXd::Index>(Cols));
-            (void) out;  // dummy to suppress unused var warning
-
-            stan::math::initialize(out, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(out,DUMMY_VAR__);
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 657;
-            if (as_bool(logical_neq(rows(x),(Rows * Cols)))) {
-                current_statement_begin__ = 657;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "x is the wrong length";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 657;
-            current_statement_begin__ = 658;
-            for (int c = 1; c <= Cols; ++c) {
-                current_statement_begin__ = 658;
-                for (int r = 1; r <= Rows; ++r) {
-
-                    current_statement_begin__ = 659;
-                    stan::math::assign(get_base1_lhs(out,r,c,"out",1), get_base1(x,pos,"x",1));
-                    current_statement_begin__ = 660;
-                    stan::math::assign(pos, (pos + 1));
-                }
-            }
-            current_statement_begin__ = 662;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(out);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct reshape_vec_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,Eigen::Dynamic>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& x,
-                const int& Rows,
-                const int& Cols, std::ostream* pstream__) const {
-        return reshape_vec(x, Rows, Cols, pstream__);
-    }
-};
-
-double
-make_lower(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 673;
-        if (as_bool(logical_eq(family,1))) {
-            current_statement_begin__ = 673;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-        }
-        current_statement_begin__ = 674;
-        if (as_bool(logical_lte(family,3))) {
-
-            current_statement_begin__ = 675;
-            if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 675;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            current_statement_begin__ = 676;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        current_statement_begin__ = 678;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_lower_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_lower(family, link, pstream__);
-    }
-};
-
-double
-make_upper(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 689;
-        if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-            current_statement_begin__ = 689;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        current_statement_begin__ = 690;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::positive_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_upper_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_upper(family, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T5__>::type, Eigen::Dynamic,1>
-test_csr_matrix_times_vector(const int& m,
-                                 const int& n,
-                                 const Eigen::Matrix<T2__, Eigen::Dynamic,1>& w,
-                                 const std::vector<int>& v,
-                                 const std::vector<int>& u,
-                                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& b, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T5__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 706;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(csr_matrix_times_vector(m,n,w,v,u,b));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct test_csr_matrix_times_vector_functor__ {
-    template <typename T2__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T5__>::type, Eigen::Dynamic,1>
-    operator()(const int& m,
-                                 const int& n,
-                                 const Eigen::Matrix<T2__, Eigen::Dynamic,1>& w,
-                                 const std::vector<int>& v,
-                                 const std::vector<int>& u,
-                                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& b, std::ostream* pstream__) const {
-        return test_csr_matrix_times_vector(m, n, w, v, u, b, pstream__);
-    }
-};
-
-class model_continuous : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    int len_y;
-    double lb_y;
-    double ub_y;
-    vector_d y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    int has_intercept_z;
-    int link_phi;
-    int z_dim;
-    matrix_d betareg_z;
-    row_vector_d zbar;
-    int prior_dist_z;
-    int prior_dist_for_intercept_z;
-    vector_d prior_scale_z;
-    double prior_scale_for_intercept_z;
-    vector_d prior_mean_z;
-    double prior_mean_for_intercept_z;
-    vector_d prior_df_z;
-    double prior_df_for_intercept_z;
-    double global_prior_scale_z;
-    vector<int> num_normals_z;
-    int SSfun;
-    vector_d input;
-    vector_d Dose;
-    vector_d sqrt_y;
-    vector_d log_y;
-    double sum_log_y;
-    vector<vector<int> > V;
-    int hs_z;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_continuous(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_continuous(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_continuous_namespace::model_continuous";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_y", "int", context__.to_vec());
-        len_y = int(0);
-        vals_i__ = context__.vals_i("len_y");
-        pos__ = 0;
-        len_y = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "lb_y", "double", context__.to_vec());
-        lb_y = double(0);
-        vals_r__ = context__.vals_r("lb_y");
-        pos__ = 0;
-        lb_y = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "ub_y", "double", context__.to_vec());
-        ub_y = double(0);
-        vals_r__ = context__.vals_r("ub_y");
-        pos__ = 0;
-        ub_y = vals_r__[pos__++];
-        validate_non_negative_index("y", "len_y", len_y);
-        context__.validate_dims("data initialization", "y", "vector_d", context__.to_vec(len_y));
-        validate_non_negative_index("y", "len_y", len_y);
-        y = vector_d(static_cast<Eigen::VectorXd::Index>(len_y));
-        vals_r__ = context__.vals_r("y");
-        pos__ = 0;
-        size_t y_i_vec_lim__ = len_y;
-        for (size_t i_vec__ = 0; i_vec__ < y_i_vec_lim__; ++i_vec__) {
-            y[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept_z", "int", context__.to_vec());
-        has_intercept_z = int(0);
-        vals_i__ = context__.vals_i("has_intercept_z");
-        pos__ = 0;
-        has_intercept_z = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link_phi", "int", context__.to_vec());
-        link_phi = int(0);
-        vals_i__ = context__.vals_i("link_phi");
-        pos__ = 0;
-        link_phi = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "z_dim", "int", context__.to_vec());
-        z_dim = int(0);
-        vals_i__ = context__.vals_i("z_dim");
-        pos__ = 0;
-        z_dim = vals_i__[pos__++];
-        validate_non_negative_index("betareg_z", "N", N);
-        validate_non_negative_index("betareg_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "betareg_z", "matrix_d", context__.to_vec(N,z_dim));
-        validate_non_negative_index("betareg_z", "N", N);
-        validate_non_negative_index("betareg_z", "z_dim", z_dim);
-        betareg_z = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("betareg_z");
-        pos__ = 0;
-        size_t betareg_z_m_mat_lim__ = N;
-        size_t betareg_z_n_mat_lim__ = z_dim;
-        for (size_t n_mat__ = 0; n_mat__ < betareg_z_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < betareg_z_m_mat_lim__; ++m_mat__) {
-                betareg_z(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("zbar", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "zbar", "row_vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("zbar", "z_dim", z_dim);
-        zbar = row_vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("zbar");
-        pos__ = 0;
-        size_t zbar_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < zbar_i_vec_lim__; ++i_vec__) {
-            zbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_z", "int", context__.to_vec());
-        prior_dist_z = int(0);
-        vals_i__ = context__.vals_i("prior_dist_z");
-        pos__ = 0;
-        prior_dist_z = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept_z", "int", context__.to_vec());
-        prior_dist_for_intercept_z = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept_z");
-        pos__ = 0;
-        prior_dist_for_intercept_z = vals_i__[pos__++];
-        validate_non_negative_index("prior_scale_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_scale_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_scale_z", "z_dim", z_dim);
-        prior_scale_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_scale_z");
-        pos__ = 0;
-        size_t prior_scale_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_z_i_vec_lim__; ++i_vec__) {
-            prior_scale_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept_z", "double", context__.to_vec());
-        prior_scale_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept_z");
-        pos__ = 0;
-        prior_scale_for_intercept_z = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_mean_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_mean_z", "z_dim", z_dim);
-        prior_mean_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_mean_z");
-        pos__ = 0;
-        size_t prior_mean_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_z_i_vec_lim__; ++i_vec__) {
-            prior_mean_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept_z", "double", context__.to_vec());
-        prior_mean_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept_z");
-        pos__ = 0;
-        prior_mean_for_intercept_z = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_z", "z_dim", z_dim);
-        context__.validate_dims("data initialization", "prior_df_z", "vector_d", context__.to_vec(z_dim));
-        validate_non_negative_index("prior_df_z", "z_dim", z_dim);
-        prior_df_z = vector_d(static_cast<Eigen::VectorXd::Index>(z_dim));
-        vals_r__ = context__.vals_r("prior_df_z");
-        pos__ = 0;
-        size_t prior_df_z_i_vec_lim__ = z_dim;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_z_i_vec_lim__; ++i_vec__) {
-            prior_df_z[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept_z", "double", context__.to_vec());
-        prior_df_for_intercept_z = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept_z");
-        pos__ = 0;
-        prior_df_for_intercept_z = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale_z", "double", context__.to_vec());
-        global_prior_scale_z = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale_z");
-        pos__ = 0;
-        global_prior_scale_z = vals_r__[pos__++];
-        validate_non_negative_index("num_normals_z", "(logical_eq(prior_dist_z,7) ? z_dim : 0 )", (logical_eq(prior_dist_z,7) ? z_dim : 0 ));
-        context__.validate_dims("data initialization", "num_normals_z", "int", context__.to_vec((logical_eq(prior_dist_z,7) ? z_dim : 0 )));
-        validate_non_negative_index("num_normals_z", "(logical_eq(prior_dist_z,7) ? z_dim : 0 )", (logical_eq(prior_dist_z,7) ? z_dim : 0 ));
-        num_normals_z = std::vector<int>((logical_eq(prior_dist_z,7) ? z_dim : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals_z");
-        pos__ = 0;
-        size_t num_normals_z_limit_0__ = (logical_eq(prior_dist_z,7) ? z_dim : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_z_limit_0__; ++i_0__) {
-            num_normals_z[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "SSfun", "int", context__.to_vec());
-        SSfun = int(0);
-        vals_i__ = context__.vals_i("SSfun");
-        pos__ = 0;
-        SSfun = vals_i__[pos__++];
-        validate_non_negative_index("input", "(logical_gt(SSfun,0) ? len_y : 0 )", (logical_gt(SSfun,0) ? len_y : 0 ));
-        context__.validate_dims("data initialization", "input", "vector_d", context__.to_vec((logical_gt(SSfun,0) ? len_y : 0 )));
-        validate_non_negative_index("input", "(logical_gt(SSfun,0) ? len_y : 0 )", (logical_gt(SSfun,0) ? len_y : 0 ));
-        input = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(SSfun,0) ? len_y : 0 )));
-        vals_r__ = context__.vals_r("input");
-        pos__ = 0;
-        size_t input_i_vec_lim__ = (logical_gt(SSfun,0) ? len_y : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < input_i_vec_lim__; ++i_vec__) {
-            input[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("Dose", "(logical_eq(SSfun,5) ? len_y : 0 )", (logical_eq(SSfun,5) ? len_y : 0 ));
-        context__.validate_dims("data initialization", "Dose", "vector_d", context__.to_vec((logical_eq(SSfun,5) ? len_y : 0 )));
-        validate_non_negative_index("Dose", "(logical_eq(SSfun,5) ? len_y : 0 )", (logical_eq(SSfun,5) ? len_y : 0 ));
-        Dose = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(SSfun,5) ? len_y : 0 )));
-        vals_r__ = context__.vals_r("Dose");
-        pos__ = 0;
-        size_t Dose_i_vec_lim__ = (logical_eq(SSfun,5) ? len_y : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < Dose_i_vec_lim__; ++i_vec__) {
-            Dose[i_vec__] = vals_r__[pos__++];
-        }
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"len_y",len_y,0);
-        check_greater_or_equal(function__,"ub_y",ub_y,lb_y);
-        check_greater_or_equal(function__,"y",y,lb_y);
-        check_less_or_equal(function__,"y",y,ub_y);
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        check_greater_or_equal(function__,"has_intercept_z",has_intercept_z,0);
-        check_less_or_equal(function__,"has_intercept_z",has_intercept_z,1);
-        check_greater_or_equal(function__,"link_phi",link_phi,0);
-        check_greater_or_equal(function__,"z_dim",z_dim,0);
-        check_greater_or_equal(function__,"prior_dist_z",prior_dist_z,0);
-        check_less_or_equal(function__,"prior_dist_z",prior_dist_z,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept_z",prior_dist_for_intercept_z,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept_z",prior_dist_for_intercept_z,2);
-        check_greater_or_equal(function__,"prior_scale_z",prior_scale_z,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept_z",prior_scale_for_intercept_z,0);
-        check_greater_or_equal(function__,"prior_df_z",prior_df_z,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept_z",prior_df_for_intercept_z,0);
-        check_greater_or_equal(function__,"global_prior_scale_z",global_prior_scale_z,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist_z,7) ? z_dim : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals_z[k0__]",num_normals_z[k0__],2);
-        }
-        check_greater_or_equal(function__,"SSfun",SSfun,0);
-        check_less_or_equal(function__,"SSfun",SSfun,10);
-        // initialize data variables
-        validate_non_negative_index("sqrt_y", "(logical_eq(family,3) ? len_y : 0 )", (logical_eq(family,3) ? len_y : 0 ));
-        sqrt_y = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(family,3) ? len_y : 0 )));
-        stan::math::fill(sqrt_y,DUMMY_VAR__);
-        validate_non_negative_index("log_y", "(logical_eq(family,3) ? len_y : 0 )", (logical_eq(family,3) ? len_y : 0 ));
-        log_y = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(family,3) ? len_y : 0 )));
-        stan::math::fill(log_y,DUMMY_VAR__);
-        sum_log_y = double(0);
-        stan::math::fill(sum_log_y,DUMMY_VAR__);
-        stan::math::assign(sum_log_y,(logical_eq(family,1) ? stan::math::not_a_number() : sum(log(y)) ));
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "len_y", len_y);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(len_y,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(len_y,(special_case ? t : 0 ),v, pstream__));
-        hs_z = int(0);
-        stan::math::fill(hs_z, std::numeric_limits<int>::min());
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            current_statement_begin__ = 843;
-            if (as_bool(logical_lte(prior_dist,2))) {
-                current_statement_begin__ = 843;
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                current_statement_begin__ = 844;
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                current_statement_begin__ = 845;
-                stan::math::assign(hs, 4);
-            } else {
-                current_statement_begin__ = 846;
-                stan::math::assign(hs, 0);
-            }
-            current_statement_begin__ = 848;
-            stan::math::assign(pos, 1);
-            current_statement_begin__ = 849;
-            for (int i = 1; i <= t; ++i) {
-
-                current_statement_begin__ = 850;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    current_statement_begin__ = 851;
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        current_statement_begin__ = 852;
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        current_statement_begin__ = 853;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                current_statement_begin__ = 856;
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    current_statement_begin__ = 856;
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-            current_statement_begin__ = 859;
-            if (as_bool(logical_lte(prior_dist_z,2))) {
-                current_statement_begin__ = 859;
-                stan::math::assign(hs_z, 0);
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                current_statement_begin__ = 860;
-                stan::math::assign(hs_z, 2);
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                current_statement_begin__ = 861;
-                stan::math::assign(hs_z, 4);
-            } else {
-                current_statement_begin__ = 862;
-                stan::math::assign(hs_z, 0);
-            }
-            current_statement_begin__ = 863;
-            stan::math::assign(is_continuous, 1);
-            current_statement_begin__ = 865;
-            if (as_bool(logical_eq(family,3))) {
-
-                current_statement_begin__ = 866;
-                stan::math::assign(sqrt_y, sqrt(y));
-                current_statement_begin__ = 867;
-                stan::math::assign(log_y, log(y));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < len_y; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"hs_z",hs_z,0);
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-        ++num_params_r__;
-        validate_non_negative_index("z_omega", "(logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )", (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        num_params_r__ += (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim );
-        validate_non_negative_index("gamma_z", "has_intercept_z", has_intercept_z);
-        num_params_r__ += has_intercept_z;
-        validate_non_negative_index("global_z", "hs_z", hs_z);
-        num_params_r__ += hs_z;
-        validate_non_negative_index("local_z", "z_dim", z_dim);
-        validate_non_negative_index("local_z", "hs_z", hs_z);
-        num_params_r__ += z_dim * hs_z;
-        validate_non_negative_index("S_z", "z_dim", z_dim);
-        validate_non_negative_index("S_z", "(primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)))", (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        num_params_r__ += z_dim * (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        validate_non_negative_index("one_over_lambda_z", "logical_eq(prior_dist_z,6)", logical_eq(prior_dist_z,6));
-        num_params_r__ += logical_eq(prior_dist_z,6);
-    }
-
-    ~model_continuous() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec());
-        // generate_declaration aux_unscaled
-        double aux_unscaled(0);
-        aux_unscaled = vals_r__[pos__++];
-        try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_omega")))
-            throw std::runtime_error("variable z_omega missing");
-        vals_r__ = context__.vals_r("z_omega");
-        pos__ = 0U;
-        validate_non_negative_index("z_omega", "(logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )", (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        context__.validate_dims("initialization", "z_omega", "vector_d", context__.to_vec((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )));
-        // generate_declaration z_omega
-        vector_d z_omega(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++j1__)
-            z_omega(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_omega);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_omega: ") + e.what());
-        }
-
-        if (!(context__.contains_r("gamma_z")))
-            throw std::runtime_error("variable gamma_z missing");
-        vals_r__ = context__.vals_r("gamma_z");
-        pos__ = 0U;
-        validate_non_negative_index("gamma_z", "has_intercept_z", has_intercept_z);
-        context__.validate_dims("initialization", "gamma_z", "double", context__.to_vec(has_intercept_z));
-        // generate_declaration gamma_z
-        std::vector<double> gamma_z(has_intercept_z,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept_z; ++i0__)
-            gamma_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept_z; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) ),gamma_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global_z")))
-            throw std::runtime_error("variable global_z missing");
-        vals_r__ = context__.vals_r("global_z");
-        pos__ = 0U;
-        validate_non_negative_index("global_z", "hs_z", hs_z);
-        context__.validate_dims("initialization", "global_z", "double", context__.to_vec(hs_z));
-        // generate_declaration global_z
-        std::vector<double> global_z(hs_z,double(0));
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            global_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local_z")))
-            throw std::runtime_error("variable local_z missing");
-        vals_r__ = context__.vals_r("local_z");
-        pos__ = 0U;
-        validate_non_negative_index("local_z", "hs_z", hs_z);
-        validate_non_negative_index("local_z", "z_dim", z_dim);
-        context__.validate_dims("initialization", "local_z", "vector_d", context__.to_vec(hs_z,z_dim));
-        // generate_declaration local_z
-        std::vector<vector_d> local_z(hs_z,vector_d(static_cast<Eigen::VectorXd::Index>(z_dim)));
-        for (int j1__ = 0U; j1__ < z_dim; ++j1__)
-            for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-                local_z[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs_z; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("S_z")))
-            throw std::runtime_error("variable S_z missing");
-        vals_r__ = context__.vals_r("S_z");
-        pos__ = 0U;
-        validate_non_negative_index("S_z", "(primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)))", (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        validate_non_negative_index("S_z", "z_dim", z_dim);
-        context__.validate_dims("initialization", "S_z", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))),z_dim));
-        // generate_declaration S_z
-        std::vector<vector_d> S_z((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))),vector_d(static_cast<Eigen::VectorXd::Index>(z_dim)));
-        for (int j1__ = 0U; j1__ < z_dim; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++i0__)
-                S_z[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,S_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable S_z: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda_z")))
-            throw std::runtime_error("variable one_over_lambda_z missing");
-        vals_r__ = context__.vals_r("one_over_lambda_z");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda_z", "logical_eq(prior_dist_z,6)", logical_eq(prior_dist_z,6));
-        context__.validate_dims("initialization", "one_over_lambda_z", "double", context__.to_vec(logical_eq(prior_dist_z,6)));
-        // generate_declaration one_over_lambda_z
-        std::vector<double> one_over_lambda_z(logical_eq(prior_dist_z,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist_z,6); ++i0__)
-            one_over_lambda_z[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist_z,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda_z[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda_z: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),lp__));
-            else
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-        T__ aux_unscaled;
-        (void) aux_unscaled;  // dummy to suppress unused var warning
-        if (jacobian__)
-            aux_unscaled = in__.scalar_lb_constrain(0,lp__);
-        else
-            aux_unscaled = in__.scalar_lb_constrain(0);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_omega;
-        (void) z_omega;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ),lp__);
-        else
-            z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-
-        vector<T__> gamma_z;
-        size_t dim_gamma_z_0__ = has_intercept_z;
-        gamma_z.reserve(dim_gamma_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_z_0__; ++k_0__) {
-            if (jacobian__)
-                gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) ),lp__));
-            else
-                gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) )));
-        }
-
-        vector<T__> global_z;
-        size_t dim_global_z_0__ = hs_z;
-        global_z.reserve(dim_global_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_z_0__; ++k_0__) {
-            if (jacobian__)
-                global_z.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global_z.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local_z;
-        size_t dim_local_z_0__ = hs_z;
-        local_z.reserve(dim_local_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_z_0__; ++k_0__) {
-            if (jacobian__)
-                local_z.push_back(in__.vector_lb_constrain(0,z_dim,lp__));
-            else
-                local_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > S_z;
-        size_t dim_S_z_0__ = (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        S_z.reserve(dim_S_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_S_z_0__; ++k_0__) {
-            if (jacobian__)
-                S_z.push_back(in__.vector_lb_constrain(0,z_dim,lp__));
-            else
-                S_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-
-        vector<T__> one_over_lambda_z;
-        size_t dim_one_over_lambda_z_0__ = logical_eq(prior_dist_z,6);
-        one_over_lambda_z.reserve(dim_one_over_lambda_z_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_z_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda_z.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda_z.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<T__>(aux_unscaled) : stan::math::promote_scalar<T__>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<T__>(((prior_scale_for_aux * aux_unscaled) + prior_mean_for_aux)) : stan::math::promote_scalar<T__>((prior_scale_for_aux * aux_unscaled)) )) ));
-        validate_non_negative_index("omega", "z_dim", z_dim);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  omega(static_cast<Eigen::VectorXd::Index>(z_dim));
-        (void) omega;  // dummy to suppress unused var warning
-
-        stan::math::initialize(omega, DUMMY_VAR__);
-        stan::math::fill(omega,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 908;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 908;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 909;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 910;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 911;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 914;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 915;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 916;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 919;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 920;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 921;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 924;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 926;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 929;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 930;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 931;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 932;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 933;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 934;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 936;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 940;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 941;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 942;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 942;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 943;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 946;
-            if (as_bool(logical_eq(prior_dist_z,0))) {
-                current_statement_begin__ = 946;
-                stan::math::assign(omega, z_omega);
-            } else if (as_bool(logical_eq(prior_dist_z,1))) {
-                current_statement_begin__ = 947;
-                stan::math::assign(omega, add(elt_multiply(z_omega,prior_scale_z),prior_mean_z));
-            } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                current_statement_begin__ = 948;
-                for (int k = 1; k <= z_dim; ++k) {
-
-                    current_statement_begin__ = 949;
-                    stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((CFt(get_base1(omega,k,"omega",1),get_base1(prior_df_z,k,"prior_df_z",1), pstream__) * get_base1(prior_scale_z,k,"prior_scale_z",1)) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                current_statement_begin__ = 952;
-                stan::math::assign(omega, hs_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                current_statement_begin__ = 954;
-                stan::math::assign(omega, hsplus_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,5))) {
-                current_statement_begin__ = 956;
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(prior_scale_z,sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,6))) {
-                current_statement_begin__ = 958;
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),prior_scale_z),sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 961;
-                    for (int k = 1; k <= z_dim; ++k) {
-
-                        current_statement_begin__ = 962;
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), get_base1(z_omega,z_pos,"z_omega",1));
-                        current_statement_begin__ = 963;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 964;
-                        for (int n = 2; n <= get_base1(num_normals_z,k,"num_normals_z",1); ++n) {
-
-                            current_statement_begin__ = 965;
-                            stan::math::assign(get_base1_lhs(omega,k,"omega",1), (get_base1(omega,k,"omega",1) * get_base1(z_omega,z_pos,"z_omega",1)));
-                            current_statement_begin__ = 966;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 968;
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((get_base1(omega,k,"omega",1) * pow(get_base1(prior_scale_z,k,"prior_scale_z",1),get_base1(num_normals_z,k,"num_normals_z",1))) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 973;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 974;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 976;
-                        stan::math::assign(theta_L, multiply(elt_multiply(scale,tau),aux));
-                        current_statement_begin__ = 977;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 977;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 978;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 980;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 981;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 985;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 987;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < z_dim; ++i0__) {
-            if (stan::math::is_uninitialized(omega(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: omega" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta_z", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta_z(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta_z;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta_z, DUMMY_VAR__);
-                stan::math::fill(eta_z,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 995;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 996;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 996;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 997;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 999;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 1000;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 1000;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 1001;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 1001;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 1002;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 1004;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 1004;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 1004;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 1005;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 1007;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 1008;
-                    if (as_bool((primitive_value((primitive_value(logical_eq(family,1)) || primitive_value(logical_eq(link,2)))) || primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_neq(link,5))))))) {
-                        current_statement_begin__ = 1008;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-                        current_statement_begin__ = 1009;
-                        stan::math::assign(eta, add(subtract(eta,max(eta)),get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        current_statement_begin__ = 1010;
-                        stan::math::assign(eta, add(subtract(eta,min(eta)),get_base1(gamma,1,"gamma",1)));
-                    }
-                } else {
-
-                    current_statement_begin__ = 1015;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 1018;
-                if (as_bool(logical_gt(SSfun,0))) {
-                    {
-                        validate_non_negative_index("P", "len_y", len_y);
-                        validate_non_negative_index("P", "K", K);
-                        Eigen::Matrix<T__,Eigen::Dynamic,Eigen::Dynamic>  P(static_cast<Eigen::VectorXd::Index>(len_y),static_cast<Eigen::VectorXd::Index>(K));
-                        (void) P;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(P, DUMMY_VAR__);
-                        stan::math::fill(P,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 1020;
-                        stan::math::assign(P, reshape_vec(eta,len_y,K, pstream__));
-                        current_statement_begin__ = 1021;
-                        if (as_bool(logical_lt(SSfun,5))) {
-
-                            current_statement_begin__ = 1022;
-                            if (as_bool(logical_lte(SSfun,2))) {
-
-                                current_statement_begin__ = 1023;
-                                if (as_bool(logical_eq(SSfun,1))) {
-                                    current_statement_begin__ = 1023;
-                                    lp_accum__.add(normal_log(y,SS_asymp(input,P, pstream__),aux));
-                                } else {
-                                    current_statement_begin__ = 1024;
-                                    lp_accum__.add(normal_log(y,SS_asympOff(input,P, pstream__),aux));
-                                }
-                            } else if (as_bool(logical_eq(SSfun,3))) {
-                                current_statement_begin__ = 1026;
-                                lp_accum__.add(normal_log(y,SS_asympOrig(input,P, pstream__),aux));
-                            } else {
-
-                                current_statement_begin__ = 1028;
-                                for (int i = 1; i <= len_y; ++i) {
-                                    current_statement_begin__ = 1028;
-                                    stan::math::assign(get_base1_lhs(P,i,1,"P",1), (get_base1(P,i,1,"P",1) + exp(get_base1(P,i,3,"P",1))));
-                                }
-                                current_statement_begin__ = 1029;
-                                lp_accum__.add(normal_log(y,SS_biexp(input,P, pstream__),aux));
-                            }
-                        } else {
-
-                            current_statement_begin__ = 1033;
-                            if (as_bool(logical_lte(SSfun,7))) {
-
-                                current_statement_begin__ = 1034;
-                                if (as_bool(logical_eq(SSfun,5))) {
-                                    current_statement_begin__ = 1034;
-                                    lp_accum__.add(normal_log(y,SS_fol(Dose,input,P, pstream__),aux));
-                                } else if (as_bool(logical_eq(SSfun,6))) {
-                                    current_statement_begin__ = 1035;
-                                    lp_accum__.add(normal_log(y,SS_fpl(input,P, pstream__),aux));
-                                } else {
-                                    current_statement_begin__ = 1036;
-                                    lp_accum__.add(normal_log(y,SS_gompertz(input,P, pstream__),aux));
-                                }
-                            } else {
-
-                                current_statement_begin__ = 1039;
-                                if (as_bool(logical_eq(SSfun,8))) {
-                                    current_statement_begin__ = 1039;
-                                    lp_accum__.add(normal_log(y,SS_logis(input,P, pstream__),aux));
-                                } else if (as_bool(logical_eq(SSfun,9))) {
-                                    current_statement_begin__ = 1040;
-                                    lp_accum__.add(normal_log(y,SS_micmen(input,P, pstream__),aux));
-                                } else {
-                                    current_statement_begin__ = 1041;
-                                    lp_accum__.add(normal_log(y,SS_weibull(input,P, pstream__),aux));
-                                }
-                            }
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    current_statement_begin__ = 1047;
-                    if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(z_dim,0)))) && primitive_value(logical_gt(link_phi,0))))) {
-
-                        current_statement_begin__ = 1048;
-                        stan::math::assign(eta_z, multiply(betareg_z,omega));
-                    } else if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(z_dim,0)))) && primitive_value(logical_eq(has_intercept_z,1))))) {
-
-                        current_statement_begin__ = 1051;
-                        stan::math::assign(eta_z, rep_vector(0.0,N));
-                    }
-                    current_statement_begin__ = 1054;
-                    if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                        current_statement_begin__ = 1055;
-                        if (as_bool(logical_gt(link_phi,1))) {
-
-                            current_statement_begin__ = 1056;
-                            stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),get_base1(gamma_z,1,"gamma_z",1)));
-                        } else {
-
-                            current_statement_begin__ = 1059;
-                            stan::math::assign(eta_z, add(eta_z,get_base1(gamma_z,1,"gamma_z",1)));
-                        }
-                    } else {
-
-                        current_statement_begin__ = 1064;
-                        if (as_bool(logical_gt(link_phi,1))) {
-
-                            current_statement_begin__ = 1065;
-                            stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),dot_product(zbar,omega)));
-                        } else {
-
-                            current_statement_begin__ = 1068;
-                            stan::math::assign(eta_z, add(eta_z,dot_product(zbar,omega)));
-                        }
-                    }
-                    current_statement_begin__ = 1071;
-                    if (as_bool(logical_eq(family,1))) {
-
-                        current_statement_begin__ = 1072;
-                        if (as_bool(logical_eq(link,1))) {
-                            current_statement_begin__ = 1073;
-                            lp_accum__.add(normal_log(y,eta,aux));
-                        } else if (as_bool(logical_eq(link,2))) {
-                            current_statement_begin__ = 1075;
-                            lp_accum__.add(normal_log(y,exp(eta),aux));
-                        } else {
-                            current_statement_begin__ = 1077;
-                            lp_accum__.add(normal_log(y,divide_real_by_vector(1,eta, pstream__),aux));
-                        }
-                    } else if (as_bool(logical_eq(family,2))) {
-
-                        current_statement_begin__ = 1081;
-                        lp_accum__.add(GammaReg(y,eta,aux,link,sum_log_y, pstream__));
-                    } else if (as_bool(logical_eq(family,3))) {
-
-                        current_statement_begin__ = 1084;
-                        lp_accum__.add(inv_gaussian(y,linkinv_inv_gaussian(eta,link, pstream__),aux,sum_log_y,sqrt_y, pstream__));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-                        {
-                            validate_non_negative_index("mu", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu, DUMMY_VAR__);
-                            stan::math::fill(mu,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 1089;
-                            stan::math::assign(mu, linkinv_beta(eta,link, pstream__));
-                            current_statement_begin__ = 1090;
-                            lp_accum__.add(beta_log(y,multiply(mu,aux),multiply(subtract(1,mu),aux)));
-                        }
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-                        {
-                            validate_non_negative_index("mu", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu, DUMMY_VAR__);
-                            stan::math::fill(mu,DUMMY_VAR__);
-                            validate_non_negative_index("mu_z", "N", N);
-                            Eigen::Matrix<T__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(N));
-                            (void) mu_z;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(mu_z, DUMMY_VAR__);
-                            stan::math::fill(mu_z,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 1095;
-                            stan::math::assign(mu, linkinv_beta(eta,link, pstream__));
-                            current_statement_begin__ = 1096;
-                            stan::math::assign(mu_z, linkinv_beta_z(eta_z,link_phi, pstream__));
-                            current_statement_begin__ = 1097;
-                            lp_accum__.add(beta_log(y,rows_dot_product(mu,mu_z),rows_dot_product(subtract(1,mu),mu_z)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    {
-                        validate_non_negative_index("summands", "N", N);
-                        Eigen::Matrix<T__,Eigen::Dynamic,1>  summands(static_cast<Eigen::VectorXd::Index>(N));
-                        (void) summands;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(summands, DUMMY_VAR__);
-                        stan::math::fill(summands,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 1103;
-                        if (as_bool(logical_eq(family,1))) {
-                            current_statement_begin__ = 1103;
-                            stan::math::assign(summands, pw_gauss(y,eta,aux,link, pstream__));
-                        } else if (as_bool(logical_eq(family,2))) {
-                            current_statement_begin__ = 1104;
-                            stan::math::assign(summands, pw_gamma(y,eta,aux,link, pstream__));
-                        } else if (as_bool(logical_eq(family,3))) {
-                            current_statement_begin__ = 1105;
-                            stan::math::assign(summands, pw_inv_gaussian(y,eta,aux,link,log_y,sqrt_y, pstream__));
-                        } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-                            current_statement_begin__ = 1106;
-                            stan::math::assign(summands, pw_beta(y,eta,aux,link, pstream__));
-                        } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-                            current_statement_begin__ = 1107;
-                            stan::math::assign(summands, pw_beta_z(y,eta,eta_z,link,link_phi, pstream__));
-                        }
-                        current_statement_begin__ = 1108;
-                        lp_accum__.add(dot_product(weights,summands));
-                    }
-                }
-                current_statement_begin__ = 1113;
-                if (as_bool((primitive_value(logical_gt(prior_dist_for_aux,0)) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 1115;
-                        if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                            current_statement_begin__ = 1116;
-                            lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                        } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                            current_statement_begin__ = 1118;
-                            lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                        } else {
-                            current_statement_begin__ = 1120;
-                            lp_accum__.add(exponential_log(aux_unscaled,1));
-                        }
-                    }
-                }
-                current_statement_begin__ = 1126;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 1126;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    current_statement_begin__ = 1127;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 1130;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 1131;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 1132;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 1133;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 1134;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 1138;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 1139;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 1140;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 1141;
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 1143;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        current_statement_begin__ = 1144;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 1145;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    current_statement_begin__ = 1148;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 1149;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    current_statement_begin__ = 1152;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 1153;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    current_statement_begin__ = 1154;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    current_statement_begin__ = 1157;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                current_statement_begin__ = 1162;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 1163;
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        current_statement_begin__ = 1164;
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        current_statement_begin__ = 1166;
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                current_statement_begin__ = 1171;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 1172;
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    current_statement_begin__ = 1173;
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            current_statement_begin__ = 1175;
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                current_statement_begin__ = 1176;
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                current_statement_begin__ = 1178;
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                current_statement_begin__ = 1180;
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 1185;
-                if (as_bool(logical_eq(prior_dist_z,1))) {
-                    current_statement_begin__ = 1185;
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                    current_statement_begin__ = 1186;
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 1189;
-                        lp_accum__.add(normal_log(z_omega,0,1));
-                        current_statement_begin__ = 1190;
-                        lp_accum__.add((normal_log(get_base1(local_z,1,"local_z",1),0,1) - log_half));
-                        current_statement_begin__ = 1191;
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,2,"local_z",1),multiply(0.5,prior_df_z),multiply(0.5,prior_df_z)));
-                        current_statement_begin__ = 1192;
-                        lp_accum__.add((normal_log(get_base1(global_z,1,"global_z",1),0,1) - log_half));
-                        current_statement_begin__ = 1193;
-                        lp_accum__.add(inv_gamma_log(get_base1(global_z,2,"global_z",1),0.5,0.5));
-                    }
-                } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 1197;
-                        lp_accum__.add(normal_log(z_omega,0,1));
-                        current_statement_begin__ = 1198;
-                        lp_accum__.add((normal_log(get_base1(local_z,1,"local_z",1),0,1) - log_half));
-                        current_statement_begin__ = 1199;
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,2,"local_z",1),multiply(0.5,prior_df_z),multiply(0.5,prior_df_z)));
-                        current_statement_begin__ = 1200;
-                        lp_accum__.add((normal_log(get_base1(local_z,3,"local_z",1),0,1) - log_half));
-                        current_statement_begin__ = 1202;
-                        lp_accum__.add(inv_gamma_log(get_base1(local_z,4,"local_z",1),multiply(0.5,prior_scale_z),multiply(0.5,prior_scale_z)));
-                        current_statement_begin__ = 1203;
-                        lp_accum__.add((normal_log(get_base1(global_z,1,"global_z",1),0,1) - log_half));
-                        current_statement_begin__ = 1204;
-                        lp_accum__.add(inv_gamma_log(get_base1(global_z,2,"global_z",1),0.5,0.5));
-                    }
-                } else if (as_bool(logical_eq(prior_dist_z,5))) {
-
-                    current_statement_begin__ = 1207;
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                    current_statement_begin__ = 1208;
-                    lp_accum__.add(exponential_log(get_base1(S_z,1,"S_z",1),1));
-                } else if (as_bool(logical_eq(prior_dist_z,6))) {
-
-                    current_statement_begin__ = 1211;
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                    current_statement_begin__ = 1212;
-                    lp_accum__.add(exponential_log(get_base1(S_z,1,"S_z",1),1));
-                    current_statement_begin__ = 1213;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),get_base1(prior_df_z,1,"prior_df_z",1)));
-                } else if (as_bool(logical_eq(prior_dist_z,7))) {
-
-                    current_statement_begin__ = 1216;
-                    lp_accum__.add(normal_log(z_omega,0,1));
-                }
-                current_statement_begin__ = 1221;
-                if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                    current_statement_begin__ = 1222;
-                    if (as_bool(logical_eq(prior_dist_for_intercept_z,1))) {
-                        current_statement_begin__ = 1223;
-                        lp_accum__.add(normal_log(gamma_z,prior_mean_for_intercept_z,prior_scale_for_intercept_z));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept_z,2))) {
-                        current_statement_begin__ = 1225;
-                        lp_accum__.add(student_t_log(gamma_z,prior_df_for_intercept_z,prior_mean_for_intercept_z,prior_scale_for_intercept_z));
-                    }
-                }
-                current_statement_begin__ = 1229;
-                if (as_bool(logical_gt(t,0))) {
-                    current_statement_begin__ = 1229;
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-                current_statement_begin__ = 1230;
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("aux_unscaled");
-        names__.push_back("z_omega");
-        names__.push_back("gamma_z");
-        names__.push_back("global_z");
-        names__.push_back("local_z");
-        names__.push_back("S_z");
-        names__.push_back("one_over_lambda_z");
-        names__.push_back("aux");
-        names__.push_back("omega");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("omega_int");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs_z);
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))));
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist_z,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(z_dim);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept_z);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_continuous_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        double aux_unscaled = in__.scalar_lb_constrain(0);
-        vector_d z_omega = in__.vector_constrain((logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ));
-        vector<double> gamma_z;
-        size_t dim_gamma_z_0__ = has_intercept_z;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_z_0__; ++k_0__) {
-            gamma_z.push_back(in__.scalar_lb_constrain((logical_lte(link_phi,1) ? stan::math::promote_scalar<double>(stan::math::negative_infinity()) : stan::math::promote_scalar<double>(0) )));
-        }
-        vector<double> global_z;
-        size_t dim_global_z_0__ = hs_z;
-        for (size_t k_0__ = 0; k_0__ < dim_global_z_0__; ++k_0__) {
-            global_z.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local_z;
-        size_t dim_local_z_0__ = hs_z;
-        for (size_t k_0__ = 0; k_0__ < dim_local_z_0__; ++k_0__) {
-            local_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-        vector<vector_d> S_z;
-        size_t dim_S_z_0__ = (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_S_z_0__; ++k_0__) {
-            S_z.push_back(in__.vector_lb_constrain(0,z_dim));
-        }
-        vector<double> one_over_lambda_z;
-        size_t dim_one_over_lambda_z_0__ = logical_eq(prior_dist_z,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_z_0__; ++k_0__) {
-            one_over_lambda_z.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-        vars__.push_back(aux_unscaled);
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            vars__.push_back(z_omega[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept_z; ++k_0__) {
-            vars__.push_back(gamma_z[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs_z; ++k_0__) {
-            vars__.push_back(global_z[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < z_dim; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs_z; ++k_0__) {
-                vars__.push_back(local_z[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < z_dim; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                vars__.push_back(S_z[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist_z,6); ++k_0__) {
-            vars__.push_back(one_over_lambda_z[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<double>(aux_unscaled) : stan::math::promote_scalar<double>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<double>(((prior_scale_for_aux * aux_unscaled) + prior_mean_for_aux)) : stan::math::promote_scalar<double>((prior_scale_for_aux * aux_unscaled)) )) ));
-        validate_non_negative_index("omega", "z_dim", z_dim);
-        vector_d omega(static_cast<Eigen::VectorXd::Index>(z_dim));
-        (void) omega;  // dummy to suppress unused var warning
-
-        stan::math::initialize(omega, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(omega,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 908;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 908;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 909;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 910;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 911;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 914;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 915;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 916;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 919;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 920;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 921;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 924;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 926;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 929;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 930;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 931;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 932;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 933;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 934;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 936;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 940;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 941;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 942;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 942;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 943;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 946;
-            if (as_bool(logical_eq(prior_dist_z,0))) {
-                current_statement_begin__ = 946;
-                stan::math::assign(omega, z_omega);
-            } else if (as_bool(logical_eq(prior_dist_z,1))) {
-                current_statement_begin__ = 947;
-                stan::math::assign(omega, add(elt_multiply(z_omega,prior_scale_z),prior_mean_z));
-            } else if (as_bool(logical_eq(prior_dist_z,2))) {
-                current_statement_begin__ = 948;
-                for (int k = 1; k <= z_dim; ++k) {
-
-                    current_statement_begin__ = 949;
-                    stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((CFt(get_base1(omega,k,"omega",1),get_base1(prior_df_z,k,"prior_df_z",1), pstream__) * get_base1(prior_scale_z,k,"prior_scale_z",1)) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist_z,3))) {
-                current_statement_begin__ = 952;
-                stan::math::assign(omega, hs_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,4))) {
-                current_statement_begin__ = 954;
-                stan::math::assign(omega, hsplus_prior(z_omega,global_z,local_z,global_prior_scale,1, pstream__));
-            } else if (as_bool(logical_eq(prior_dist_z,5))) {
-                current_statement_begin__ = 956;
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(prior_scale_z,sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,6))) {
-                current_statement_begin__ = 958;
-                stan::math::assign(omega, add(prior_mean_z,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda_z,1,"one_over_lambda_z",1),prior_scale_z),sqrt(multiply(2,get_base1(S_z,1,"S_z",1)))),z_omega)));
-            } else if (as_bool(logical_eq(prior_dist_z,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 961;
-                    for (int k = 1; k <= z_dim; ++k) {
-
-                        current_statement_begin__ = 962;
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), get_base1(z_omega,z_pos,"z_omega",1));
-                        current_statement_begin__ = 963;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 964;
-                        for (int n = 2; n <= get_base1(num_normals_z,k,"num_normals_z",1); ++n) {
-
-                            current_statement_begin__ = 965;
-                            stan::math::assign(get_base1_lhs(omega,k,"omega",1), (get_base1(omega,k,"omega",1) * get_base1(z_omega,z_pos,"z_omega",1)));
-                            current_statement_begin__ = 966;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 968;
-                        stan::math::assign(get_base1_lhs(omega,k,"omega",1), ((get_base1(omega,k,"omega",1) * pow(get_base1(prior_scale_z,k,"prior_scale_z",1),get_base1(num_normals_z,k,"num_normals_z",1))) + get_base1(prior_mean_z,k,"prior_mean_z",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 973;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 974;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 976;
-                        stan::math::assign(theta_L, multiply(elt_multiply(scale,tau),aux));
-                        current_statement_begin__ = 977;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 977;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 978;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 980;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 981;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 985;
-                    stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    current_statement_begin__ = 987;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < z_dim; ++k_0__) {
-            vars__.push_back(omega[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("omega_int", "has_intercept_z", has_intercept_z);
-        vector<double> omega_int(has_intercept_z, 0.0);
-        stan::math::initialize(omega_int, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(omega_int,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            current_statement_begin__ = 1236;
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                current_statement_begin__ = 1237;
-                if (as_bool(dense_X)) {
-                    current_statement_begin__ = 1237;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    current_statement_begin__ = 1238;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            current_statement_begin__ = 1240;
-            if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                current_statement_begin__ = 1241;
-                stan::math::assign(get_base1_lhs(omega_int,1,"omega_int",1), (get_base1(gamma_z,1,"gamma_z",1) - dot_product(zbar,omega)));
-            }
-            {
-                validate_non_negative_index("eta_z", "N", N);
-                vector_d eta_z(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta_z;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta_z, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta_z,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 1247;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 1248;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 1248;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 1249;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 1251;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 1252;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 1252;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 1253;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 1253;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 1254;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 1256;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 1256;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 1256;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 1257;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 1259;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 1260;
-                    if (as_bool((primitive_value(logical_eq(make_lower(family,link, pstream__),stan::math::negative_infinity())) && primitive_value(logical_eq(make_upper(family,link, pstream__),stan::math::positive_infinity()))))) {
-                        current_statement_begin__ = 1261;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,5))))) {
-                        {
-                            double max_eta(0.0);
-                            (void) max_eta;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(max_eta, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(max_eta,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 1264;
-                            stan::math::assign(max_eta, max(eta));
-                            current_statement_begin__ = 1265;
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - max_eta));
-                            current_statement_begin__ = 1266;
-                            stan::math::assign(eta, add(subtract(eta,max_eta),get_base1(gamma,1,"gamma",1)));
-                        }
-                    } else {
-                        {
-                            double min_eta(0.0);
-                            (void) min_eta;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(min_eta, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(min_eta,DUMMY_VAR__);
-                            stan::math::assign(min_eta,min(eta));
-
-
-                            current_statement_begin__ = 1270;
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - min_eta));
-                            current_statement_begin__ = 1271;
-                            stan::math::assign(eta, add(subtract(eta,min_eta),get_base1(gamma,1,"gamma",1)));
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 1277;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 1281;
-                if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(z_dim,0)))) && primitive_value(logical_gt(link_phi,0))))) {
-
-                    current_statement_begin__ = 1282;
-                    stan::math::assign(eta_z, multiply(betareg_z,omega));
-                } else if (as_bool((primitive_value((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(z_dim,0)))) && primitive_value(logical_eq(has_intercept_z,1))))) {
-
-                    current_statement_begin__ = 1285;
-                    stan::math::assign(eta_z, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 1288;
-                if (as_bool(logical_eq(has_intercept_z,1))) {
-
-                    current_statement_begin__ = 1289;
-                    if (as_bool(logical_gt(link_phi,1))) {
-
-                        current_statement_begin__ = 1290;
-                        stan::math::assign(get_base1_lhs(omega_int,1,"omega_int",1), (get_base1(omega_int,1,"omega_int",1) - min(eta_z)));
-                        current_statement_begin__ = 1291;
-                        stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),get_base1(gamma_z,1,"gamma_z",1)));
-                    } else {
-
-                        current_statement_begin__ = 1294;
-                        stan::math::assign(eta_z, add(eta_z,get_base1(gamma_z,1,"gamma_z",1)));
-                    }
-                } else {
-
-                    current_statement_begin__ = 1299;
-                    if (as_bool(logical_gt(link_phi,1))) {
-
-                        current_statement_begin__ = 1300;
-                        stan::math::assign(eta_z, add(subtract(eta_z,min(eta_z)),dot_product(zbar,omega)));
-                    } else {
-
-                        current_statement_begin__ = 1303;
-                        stan::math::assign(eta_z, add(eta_z,dot_product(zbar,omega)));
-                    }
-                }
-                current_statement_begin__ = 1307;
-                if (as_bool(logical_gt(SSfun,0))) {
-                    {
-                        validate_non_negative_index("eta_nlmer", "len_y", len_y);
-                        vector_d eta_nlmer(static_cast<Eigen::VectorXd::Index>(len_y));
-                        (void) eta_nlmer;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(eta_nlmer, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(eta_nlmer,DUMMY_VAR__);
-                        validate_non_negative_index("P", "len_y", len_y);
-                        validate_non_negative_index("P", "K", K);
-                        matrix_d P(static_cast<Eigen::VectorXd::Index>(len_y),static_cast<Eigen::VectorXd::Index>(K));
-                        (void) P;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(P, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(P,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 1310;
-                        stan::math::assign(P, reshape_vec(eta,len_y,K, pstream__));
-                        current_statement_begin__ = 1311;
-                        if (as_bool(logical_lt(SSfun,5))) {
-
-                            current_statement_begin__ = 1312;
-                            if (as_bool(logical_lte(SSfun,2))) {
-
-                                current_statement_begin__ = 1313;
-                                if (as_bool(logical_eq(SSfun,1))) {
-                                    current_statement_begin__ = 1313;
-                                    stan::math::assign(eta_nlmer, SS_asymp(input,P, pstream__));
-                                } else {
-                                    current_statement_begin__ = 1314;
-                                    stan::math::assign(eta_nlmer, SS_asympOff(input,P, pstream__));
-                                }
-                            } else if (as_bool(logical_eq(SSfun,3))) {
-                                current_statement_begin__ = 1316;
-                                stan::math::assign(eta_nlmer, SS_asympOrig(input,P, pstream__));
-                            } else {
-                                current_statement_begin__ = 1317;
-                                stan::math::assign(eta_nlmer, SS_biexp(input,P, pstream__));
-                            }
-                        } else {
-
-                            current_statement_begin__ = 1320;
-                            if (as_bool(logical_lte(SSfun,7))) {
-
-                                current_statement_begin__ = 1321;
-                                if (as_bool(logical_eq(SSfun,5))) {
-                                    current_statement_begin__ = 1321;
-                                    stan::math::assign(eta_nlmer, SS_fol(Dose,input,P, pstream__));
-                                } else if (as_bool(logical_eq(SSfun,6))) {
-                                    current_statement_begin__ = 1322;
-                                    stan::math::assign(eta_nlmer, SS_fpl(input,P, pstream__));
-                                } else {
-                                    current_statement_begin__ = 1323;
-                                    stan::math::assign(eta_nlmer, SS_gompertz(input,P, pstream__));
-                                }
-                            } else {
-
-                                current_statement_begin__ = 1326;
-                                if (as_bool(logical_eq(SSfun,8))) {
-                                    current_statement_begin__ = 1326;
-                                    stan::math::assign(eta_nlmer, SS_logis(input,P, pstream__));
-                                } else if (as_bool(logical_eq(SSfun,9))) {
-                                    current_statement_begin__ = 1327;
-                                    stan::math::assign(eta_nlmer, SS_micmen(input,P, pstream__));
-                                } else {
-                                    current_statement_begin__ = 1328;
-                                    stan::math::assign(eta_nlmer, SS_weibull(input,P, pstream__));
-                                }
-                            }
-                        }
-                        current_statement_begin__ = 1331;
-                        for (int n = 1; n <= len_y; ++n) {
-                            current_statement_begin__ = 1331;
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta_nlmer,n,"eta_nlmer",1),aux, base_rng__)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,1))) {
-
-                    current_statement_begin__ = 1334;
-                    if (as_bool(logical_gt(link,1))) {
-                        current_statement_begin__ = 1334;
-                        stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    }
-                    current_statement_begin__ = 1335;
-                    for (int n = 1; n <= len_y; ++n) {
-                        current_statement_begin__ = 1335;
-                        stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),aux, base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    current_statement_begin__ = 1338;
-                    if (as_bool(logical_gt(link,1))) {
-                        current_statement_begin__ = 1338;
-                        stan::math::assign(eta, linkinv_gamma(eta,link, pstream__));
-                    }
-                    current_statement_begin__ = 1339;
-                    for (int n = 1; n <= len_y; ++n) {
-                        current_statement_begin__ = 1339;
-                        stan::math::assign(mean_PPD, (mean_PPD + gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    current_statement_begin__ = 1342;
-                    if (as_bool(logical_gt(link,1))) {
-                        current_statement_begin__ = 1342;
-                        stan::math::assign(eta, linkinv_inv_gaussian(eta,link, pstream__));
-                    }
-                    current_statement_begin__ = 1343;
-                    for (int n = 1; n <= len_y; ++n) {
-                        current_statement_begin__ = 1343;
-                        stan::math::assign(mean_PPD, (mean_PPD + inv_gaussian_rng(get_base1(eta,n,"eta",1),aux, base_rng__, pstream__)));
-                    }
-                } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link_phi,0))))) {
-
-                    current_statement_begin__ = 1346;
-                    stan::math::assign(eta, linkinv_beta(eta,link, pstream__));
-                    current_statement_begin__ = 1347;
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double eta_n(0.0);
-                            (void) eta_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(eta_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(eta_n,DUMMY_VAR__);
-                            stan::math::assign(eta_n,get_base1(eta,n,"eta",1));
-
-
-                            current_statement_begin__ = 1349;
-                            if (as_bool(logical_lte(aux,0))) {
-                                current_statement_begin__ = 1349;
-                                stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(0.5, base_rng__)));
-                            } else if (as_bool(logical_gte(eta_n,1))) {
-                                current_statement_begin__ = 1350;
-                                stan::math::assign(mean_PPD, (mean_PPD + 1));
-                            } else if (as_bool(logical_gt(eta_n,0))) {
-                                current_statement_begin__ = 1352;
-                                stan::math::assign(mean_PPD, (mean_PPD + beta_rng((get_base1(eta,n,"eta",1) * aux),((1 - get_base1(eta,n,"eta",1)) * aux), base_rng__)));
-                            }
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_gt(link_phi,0))))) {
-
-                    current_statement_begin__ = 1356;
-                    stan::math::assign(eta, linkinv_beta(eta,link, pstream__));
-                    current_statement_begin__ = 1357;
-                    stan::math::assign(eta_z, linkinv_beta_z(eta_z,link_phi, pstream__));
-                    current_statement_begin__ = 1358;
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double eta_n(0.0);
-                            (void) eta_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(eta_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(eta_n,DUMMY_VAR__);
-                            stan::math::assign(eta_n,get_base1(eta,n,"eta",1));
-                            double aux_n(0.0);
-                            (void) aux_n;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(aux_n, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(aux_n,DUMMY_VAR__);
-                            stan::math::assign(aux_n,get_base1(eta_z,n,"eta_z",1));
-
-
-                            current_statement_begin__ = 1361;
-                            if (as_bool(logical_lte(aux_n,0))) {
-                                current_statement_begin__ = 1361;
-                                stan::math::assign(mean_PPD, (mean_PPD + bernoulli_rng(0.5, base_rng__)));
-                            } else if (as_bool(logical_gte(eta_n,1))) {
-                                current_statement_begin__ = 1362;
-                                stan::math::assign(mean_PPD, (mean_PPD + 1));
-                            } else if (as_bool(logical_gt(eta_n,0))) {
-                                current_statement_begin__ = 1364;
-                                stan::math::assign(mean_PPD, (mean_PPD + beta_rng((eta_n * aux_n),((1 - eta_n) * aux_n), base_rng__)));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 1367;
-                stan::math::assign(mean_PPD, (mean_PPD / len_y));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept_z; ++k_0__) {
-            vars__.push_back(omega_int[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_continuous";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux_unscaled";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "S_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist_z,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= z_dim; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega_int" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux_unscaled";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist_z,7) ? sum(num_normals_z) : z_dim ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs_z; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= z_dim; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist_z,5)) || primitive_value(logical_eq(prior_dist_z,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "S_z" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist_z,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda_z" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= z_dim; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= has_intercept_z; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "omega_int" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_count_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_count");
-    reader.add_event(735, 735, "end", "model_count");
-    return reader;
-}
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 52;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 54;
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        current_statement_begin__ = 55;
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        current_statement_begin__ = 57;
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            current_statement_begin__ = 65;
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            current_statement_begin__ = 68;
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            current_statement_begin__ = 69;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 70;
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            current_statement_begin__ = 73;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 74;
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            current_statement_begin__ = 75;
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            current_statement_begin__ = 76;
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            current_statement_begin__ = 77;
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            current_statement_begin__ = 79;
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    current_statement_begin__ = 83;
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    current_statement_begin__ = 84;
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    current_statement_begin__ = 85;
-                                    for (int c = 1; c <= r; ++c) {
-                                        current_statement_begin__ = 85;
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    current_statement_begin__ = 86;
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    current_statement_begin__ = 87;
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 91;
-                            for (int c = 1; c <= nc; ++c) {
-                                current_statement_begin__ = 91;
-                                for (int r = c; r <= nc; ++r) {
-
-                                    current_statement_begin__ = 92;
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    current_statement_begin__ = 93;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 97;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 115;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 117;
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            current_statement_begin__ = 119;
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                current_statement_begin__ = 120;
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            current_statement_begin__ = 121;
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            current_statement_begin__ = 122;
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            current_statement_begin__ = 126;
-                            for (int c = 1; c <= nc; ++c) {
-
-                                current_statement_begin__ = 127;
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                current_statement_begin__ = 128;
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                current_statement_begin__ = 129;
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    current_statement_begin__ = 130;
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    current_statement_begin__ = 131;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 134;
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    current_statement_begin__ = 136;
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    current_statement_begin__ = 137;
-                                    for (int s = 1; s <= nc; ++s) {
-                                        current_statement_begin__ = 137;
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    current_statement_begin__ = 138;
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 142;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            current_statement_begin__ = 165;
-            lp_accum__.add(normal_log(z_b,0,1));
-            current_statement_begin__ = 166;
-            lp_accum__.add(normal_log(z_T,0,1));
-            current_statement_begin__ = 167;
-            for (int i = 1; i <= t; ++i) {
-                current_statement_begin__ = 167;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        current_statement_begin__ = 171;
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        current_statement_begin__ = 172;
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        current_statement_begin__ = 173;
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        current_statement_begin__ = 174;
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            current_statement_begin__ = 175;
-                            stan::math::assign(nu, (nu - 0.5));
-                            current_statement_begin__ = 176;
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            current_statement_begin__ = 177;
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        current_statement_begin__ = 179;
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        current_statement_begin__ = 180;
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 182;
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            current_statement_begin__ = 183;
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            current_statement_begin__ = 200;
-            stan::math::assign(K, rows(z_beta));
-            current_statement_begin__ = 201;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 201;
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            current_statement_begin__ = 202;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 218;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 233;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 233;
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            current_statement_begin__ = 234;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            current_statement_begin__ = 256;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 271;
-            if (as_bool(logical_gt(t,0))) {
-                current_statement_begin__ = 271;
-                for (int j = 1; j <= N; ++j) {
-                    current_statement_begin__ = 271;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 272;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        current_statement_begin__ = 273;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 275;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_count(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 288;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 288;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 289;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 290;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            current_statement_begin__ = 291;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 291;
-        current_statement_begin__ = 292;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_count_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_count(eta, link, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_pois(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 306;
-            if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 307;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 307;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log_log(get_base1(y,n,"y",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,3))) {
-                {
-                    validate_non_negative_index("phi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) phi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(phi,DUMMY_VAR__);
-                    stan::math::assign(phi,linkinv_count(eta,link, pstream__));
-
-
-                    current_statement_begin__ = 310;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 310;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log(get_base1(y,n,"y",1),get_base1(phi,n,"phi",1)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 312;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 312;
-            current_statement_begin__ = 313;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_pois_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_pois(y, eta, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-pw_nb(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("rho", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  rho(static_cast<Eigen::VectorXd::Index>(N));
-            (void) rho;  // dummy to suppress unused var warning
-
-            stan::math::initialize(rho, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(rho,DUMMY_VAR__);
-            stan::math::assign(rho,linkinv_count(eta,link, pstream__));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 329;
-            for (int n = 1; n <= N; ++n) {
-                current_statement_begin__ = 329;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), neg_binomial_2_log(get_base1(y,n,"y",1),get_base1(rho,n,"rho",1),theta));
-            }
-            current_statement_begin__ = 330;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_nb_functor__ {
-    template <typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) const {
-        return pw_nb(y, eta, theta, link, pstream__);
-    }
-};
-
-class model_count : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    vector<int> y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    vector_d prior_scale;
-    double prior_scale_for_intercept;
-    double prior_scale_for_aux;
-    vector_d prior_scale_for_smooth;
-    vector_d prior_mean;
-    double prior_mean_for_intercept;
-    double prior_mean_for_aux;
-    vector_d prior_mean_for_smooth;
-    vector_d prior_df;
-    double prior_df_for_intercept;
-    double prior_df_for_aux;
-    vector_d prior_df_for_smooth;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int t;
-    vector<int> p;
-    vector<int> l;
-    int q;
-    int len_theta_L;
-    vector_d shape;
-    vector_d scale;
-    int len_concentration;
-    vector<double> concentration;
-    int len_regularization;
-    vector<double> regularization;
-    int num_non_zero;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int special_case;
-    double poisson_max;
-    vector<vector<int> > V;
-    int len_z_T;
-    int len_var_group;
-    int len_rho;
-    int is_continuous;
-    int pos;
-    vector<double> delta;
-    int hs;
-public:
-    model_count(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_count(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_count_namespace::model_count";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_scale_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_scale_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_scale_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_scale_for_smooth");
-        pos__ = 0;
-        size_t prior_scale_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_scale_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_mean_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_mean_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_mean_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_mean_for_smooth");
-        pos__ = 0;
-        size_t prior_mean_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_mean_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        context__.validate_dims("data initialization", "prior_df_for_smooth", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        validate_non_negative_index("prior_df_for_smooth", "(logical_gt(K_smooth,0) ? max(smooth_map) : 0 )", (logical_gt(K_smooth,0) ? max(smooth_map) : 0 ));
-        prior_df_for_smooth = vector_d(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? max(smooth_map) : 0 )));
-        vals_r__ = context__.vals_r("prior_df_for_smooth");
-        pos__ = 0;
-        size_t prior_df_for_smooth_i_vec_lim__ = (logical_gt(K_smooth,0) ? max(smooth_map) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_for_smooth_i_vec_lim__; ++i_vec__) {
-            prior_df_for_smooth[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "t", "int", context__.to_vec());
-        t = int(0);
-        vals_i__ = context__.vals_i("t");
-        pos__ = 0;
-        t = vals_i__[pos__++];
-        validate_non_negative_index("p", "t", t);
-        context__.validate_dims("data initialization", "p", "int", context__.to_vec(t));
-        validate_non_negative_index("p", "t", t);
-        p = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("p");
-        pos__ = 0;
-        size_t p_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < p_limit_0__; ++i_0__) {
-            p[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("l", "t", t);
-        context__.validate_dims("data initialization", "l", "int", context__.to_vec(t));
-        validate_non_negative_index("l", "t", t);
-        l = std::vector<int>(t,int(0));
-        vals_i__ = context__.vals_i("l");
-        pos__ = 0;
-        size_t l_limit_0__ = t;
-        for (size_t i_0__ = 0; i_0__ < l_limit_0__; ++i_0__) {
-            l[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "q", "int", context__.to_vec());
-        q = int(0);
-        vals_i__ = context__.vals_i("q");
-        pos__ = 0;
-        q = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "len_theta_L", "int", context__.to_vec());
-        len_theta_L = int(0);
-        vals_i__ = context__.vals_i("len_theta_L");
-        pos__ = 0;
-        len_theta_L = vals_i__[pos__++];
-        validate_non_negative_index("shape", "t", t);
-        context__.validate_dims("data initialization", "shape", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("shape", "t", t);
-        shape = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        size_t shape_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < shape_i_vec_lim__; ++i_vec__) {
-            shape[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("scale", "t", t);
-        context__.validate_dims("data initialization", "scale", "vector_d", context__.to_vec(t));
-        validate_non_negative_index("scale", "t", t);
-        scale = vector_d(static_cast<Eigen::VectorXd::Index>(t));
-        vals_r__ = context__.vals_r("scale");
-        pos__ = 0;
-        size_t scale_i_vec_lim__ = t;
-        for (size_t i_vec__ = 0; i_vec__ < scale_i_vec_lim__; ++i_vec__) {
-            scale[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_concentration", "int", context__.to_vec());
-        len_concentration = int(0);
-        vals_i__ = context__.vals_i("len_concentration");
-        pos__ = 0;
-        len_concentration = vals_i__[pos__++];
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        context__.validate_dims("data initialization", "concentration", "double", context__.to_vec(len_concentration));
-        validate_non_negative_index("concentration", "len_concentration", len_concentration);
-        concentration = std::vector<double>(len_concentration,double(0));
-        vals_r__ = context__.vals_r("concentration");
-        pos__ = 0;
-        size_t concentration_limit_0__ = len_concentration;
-        for (size_t i_0__ = 0; i_0__ < concentration_limit_0__; ++i_0__) {
-            concentration[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "len_regularization", "int", context__.to_vec());
-        len_regularization = int(0);
-        vals_i__ = context__.vals_i("len_regularization");
-        pos__ = 0;
-        len_regularization = vals_i__[pos__++];
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec(len_regularization));
-        validate_non_negative_index("regularization", "len_regularization", len_regularization);
-        regularization = std::vector<double>(len_regularization,double(0));
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        size_t regularization_limit_0__ = len_regularization;
-        for (size_t i_0__ = 0; i_0__ < regularization_limit_0__; ++i_0__) {
-            regularization[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "num_non_zero", "int", context__.to_vec());
-        num_non_zero = int(0);
-        vals_i__ = context__.vals_i("num_non_zero");
-        pos__ = 0;
-        num_non_zero = vals_i__[pos__++];
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec(num_non_zero));
-        validate_non_negative_index("w", "num_non_zero", num_non_zero);
-        w = vector_d(static_cast<Eigen::VectorXd::Index>(num_non_zero));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = num_non_zero;
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec(num_non_zero));
-        validate_non_negative_index("v", "num_non_zero", num_non_zero);
-        v = std::vector<int>(num_non_zero,int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = num_non_zero;
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_gt(t,0) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_gt(t,0) ? (N + 1) : 0 )", (logical_gt(t,0) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_gt(t,0) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_gt(t,0) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "special_case", "int", context__.to_vec());
-        special_case = int(0);
-        vals_i__ = context__.vals_i("special_case");
-        pos__ = 0;
-        special_case = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_smooth",prior_scale_for_smooth,0);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_mean_for_smooth",prior_mean_for_smooth,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_smooth",prior_df_for_smooth,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"t",t,0);
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"p[k0__]",p[k0__],1);
-        }
-        for (int k0__ = 0; k0__ < t; ++k0__) {
-            check_greater_or_equal(function__,"l[k0__]",l[k0__],1);
-        }
-        check_greater_or_equal(function__,"q",q,0);
-        check_greater_or_equal(function__,"len_theta_L",len_theta_L,0);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"scale",scale,0);
-        check_greater_or_equal(function__,"len_concentration",len_concentration,0);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"concentration[k0__]",concentration[k0__],0);
-        }
-        check_greater_or_equal(function__,"len_regularization",len_regularization,0);
-        for (int k0__ = 0; k0__ < len_regularization; ++k0__) {
-            check_greater_or_equal(function__,"regularization[k0__]",regularization[k0__],0);
-        }
-        check_greater_or_equal(function__,"num_non_zero",num_non_zero,0);
-        for (int k0__ = 0; k0__ < num_non_zero; ++k0__) {
-            check_greater_or_equal(function__,"v[k0__]",v[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (logical_gt(t,0) ? (N + 1) : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"u[k0__]",u[k0__],0);
-        }
-        check_greater_or_equal(function__,"special_case",special_case,0);
-        check_less_or_equal(function__,"special_case",special_case,1);
-        // initialize data variables
-        poisson_max = double(0);
-        stan::math::fill(poisson_max,DUMMY_VAR__);
-        stan::math::assign(poisson_max,pow(2.0,30.0));
-        validate_non_negative_index("V", "(special_case ? t : 0 )", (special_case ? t : 0 ));
-        validate_non_negative_index("V", "N", N);
-        V = std::vector<std::vector<int> >((special_case ? t : 0 ),std::vector<int>(N,int(0)));
-        stan::math::fill(V, std::numeric_limits<int>::min());
-        stan::math::assign(V,make_V(N,(special_case ? t : 0 ),v, pstream__));
-        len_z_T = int(0);
-        stan::math::fill(len_z_T, std::numeric_limits<int>::min());
-        stan::math::assign(len_z_T,0);
-        len_var_group = int(0);
-        stan::math::fill(len_var_group, std::numeric_limits<int>::min());
-        stan::math::assign(len_var_group,(sum(p) * logical_gt(t,0)));
-        len_rho = int(0);
-        stan::math::fill(len_rho, std::numeric_limits<int>::min());
-        stan::math::assign(len_rho,(sum(p) - t));
-        is_continuous = int(0);
-        stan::math::fill(is_continuous, std::numeric_limits<int>::min());
-        stan::math::assign(is_continuous,0);
-        pos = int(0);
-        stan::math::fill(pos, std::numeric_limits<int>::min());
-        stan::math::assign(pos,1);
-        validate_non_negative_index("delta", "len_concentration", len_concentration);
-        delta = std::vector<double>(len_concentration,double(0));
-        stan::math::fill(delta,DUMMY_VAR__);
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-
-        try {
-            current_statement_begin__ = 439;
-            if (as_bool(logical_lte(prior_dist,2))) {
-                current_statement_begin__ = 439;
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                current_statement_begin__ = 440;
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                current_statement_begin__ = 441;
-                stan::math::assign(hs, 4);
-            } else {
-                current_statement_begin__ = 442;
-                stan::math::assign(hs, 0);
-            }
-            current_statement_begin__ = 444;
-            stan::math::assign(pos, 1);
-            current_statement_begin__ = 445;
-            for (int i = 1; i <= t; ++i) {
-
-                current_statement_begin__ = 446;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-
-                    current_statement_begin__ = 447;
-                    for (int j = 1; j <= get_base1(p,i,"p",1); ++j) {
-
-                        current_statement_begin__ = 448;
-                        stan::math::assign(get_base1_lhs(delta,pos,"delta",1), get_base1(concentration,j,"concentration",1));
-                        current_statement_begin__ = 449;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-                current_statement_begin__ = 452;
-                for (int j = 3; j <= get_base1(p,i,"p",1); ++j) {
-                    current_statement_begin__ = 452;
-                    stan::math::assign(len_z_T, ((len_z_T + get_base1(p,i,"p",1)) - 1));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        for (int k0__ = 0; k0__ < (special_case ? t : 0 ); ++k0__) {
-            for (int k1__ = 0; k1__ < N; ++k1__) {
-                check_greater_or_equal(function__,"V[k0__][k1__]",V[k0__][k1__],1);
-            }
-        }
-        check_greater_or_equal(function__,"len_z_T",len_z_T,0);
-        check_greater_or_equal(function__,"len_var_group",len_var_group,0);
-        check_greater_or_equal(function__,"len_rho",len_rho,0);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"pos",pos,1);
-        for (int k0__ = 0; k0__ < len_concentration; ++k0__) {
-            check_greater_or_equal(function__,"delta[k0__]",delta[k0__],0);
-        }
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        num_params_r__ += (logical_eq(prior_dist,7) ? sum(num_normals) : K );
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        num_params_r__ += K_smooth;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        num_params_r__ += (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 );
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-        validate_non_negative_index("z_b", "q", q);
-        num_params_r__ += q;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        num_params_r__ += len_z_T;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        num_params_r__ += len_rho;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        num_params_r__ += len_concentration;
-        validate_non_negative_index("tau", "t", t);
-        num_params_r__ += t;
-        validate_non_negative_index("aux_unscaled", "logical_gt(family,1)", logical_gt(family,1));
-        num_params_r__ += logical_gt(family,1);
-        validate_non_negative_index("noise", "N", N);
-        validate_non_negative_index("noise", "logical_eq(family,3)", logical_eq(family,3));
-        num_params_r__ += N * logical_eq(family,3);
-    }
-
-    ~model_count() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 ),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "(logical_eq(prior_dist,7) ? sum(num_normals) : K )", (logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>((logical_eq(prior_dist,7) ? sum(num_normals) : K )));
-        for (int j1__ = 0U; j1__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta_smooth")))
-            throw std::runtime_error("variable z_beta_smooth missing");
-        vals_r__ = context__.vals_r("z_beta_smooth");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta_smooth", "K_smooth", K_smooth);
-        context__.validate_dims("initialization", "z_beta_smooth", "vector_d", context__.to_vec(K_smooth));
-        // generate_declaration z_beta_smooth
-        vector_d z_beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        for (int j1__ = 0U; j1__ < K_smooth; ++j1__)
-            z_beta_smooth(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta_smooth);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta_smooth: ") + e.what());
-        }
-
-        if (!(context__.contains_r("smooth_sd_raw")))
-            throw std::runtime_error("variable smooth_sd_raw missing");
-        vals_r__ = context__.vals_r("smooth_sd_raw");
-        pos__ = 0U;
-        validate_non_negative_index("smooth_sd_raw", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        context__.validate_dims("initialization", "smooth_sd_raw", "vector_d", context__.to_vec((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        // generate_declaration smooth_sd_raw
-        vector_d smooth_sd_raw(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        for (int j1__ = 0U; j1__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++j1__)
-            smooth_sd_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,smooth_sd_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable smooth_sd_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_b")))
-            throw std::runtime_error("variable z_b missing");
-        vals_r__ = context__.vals_r("z_b");
-        pos__ = 0U;
-        validate_non_negative_index("z_b", "q", q);
-        context__.validate_dims("initialization", "z_b", "vector_d", context__.to_vec(q));
-        // generate_declaration z_b
-        vector_d z_b(static_cast<Eigen::VectorXd::Index>(q));
-        for (int j1__ = 0U; j1__ < q; ++j1__)
-            z_b(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_b);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_b: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_T")))
-            throw std::runtime_error("variable z_T missing");
-        vals_r__ = context__.vals_r("z_T");
-        pos__ = 0U;
-        validate_non_negative_index("z_T", "len_z_T", len_z_T);
-        context__.validate_dims("initialization", "z_T", "vector_d", context__.to_vec(len_z_T));
-        // generate_declaration z_T
-        vector_d z_T(static_cast<Eigen::VectorXd::Index>(len_z_T));
-        for (int j1__ = 0U; j1__ < len_z_T; ++j1__)
-            z_T(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_T);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_T: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "len_rho", len_rho);
-        context__.validate_dims("initialization", "rho", "vector_d", context__.to_vec(len_rho));
-        // generate_declaration rho
-        vector_d rho(static_cast<Eigen::VectorXd::Index>(len_rho));
-        for (int j1__ = 0U; j1__ < len_rho; ++j1__)
-            rho(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lub_unconstrain(0,1,rho);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("zeta")))
-            throw std::runtime_error("variable zeta missing");
-        vals_r__ = context__.vals_r("zeta");
-        pos__ = 0U;
-        validate_non_negative_index("zeta", "len_concentration", len_concentration);
-        context__.validate_dims("initialization", "zeta", "vector_d", context__.to_vec(len_concentration));
-        // generate_declaration zeta
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>(len_concentration));
-        for (int j1__ = 0U; j1__ < len_concentration; ++j1__)
-            zeta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,zeta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable zeta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        validate_non_negative_index("tau", "t", t);
-        context__.validate_dims("initialization", "tau", "vector_d", context__.to_vec(t));
-        // generate_declaration tau
-        vector_d tau(static_cast<Eigen::VectorXd::Index>(t));
-        for (int j1__ = 0U; j1__ < t; ++j1__)
-            tau(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        validate_non_negative_index("aux_unscaled", "logical_gt(family,1)", logical_gt(family,1));
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec(logical_gt(family,1)));
-        // generate_declaration aux_unscaled
-        std::vector<double> aux_unscaled(logical_gt(family,1),double(0));
-        for (int i0__ = 0U; i0__ < logical_gt(family,1); ++i0__)
-            aux_unscaled[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_gt(family,1); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("noise")))
-            throw std::runtime_error("variable noise missing");
-        vals_r__ = context__.vals_r("noise");
-        pos__ = 0U;
-        validate_non_negative_index("noise", "logical_eq(family,3)", logical_eq(family,3));
-        validate_non_negative_index("noise", "N", N);
-        context__.validate_dims("initialization", "noise", "vector_d", context__.to_vec(logical_eq(family,3),N));
-        // generate_declaration noise
-        std::vector<vector_d> noise(logical_eq(family,3),vector_d(static_cast<Eigen::VectorXd::Index>(N)));
-        for (int j1__ = 0U; j1__ < N; ++j1__)
-            for (int i0__ = 0U; i0__ < logical_eq(family,3); ++i0__)
-                noise[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(family,3); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,noise[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable noise: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 ),lp__));
-            else
-                gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 )));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ),lp__);
-        else
-            z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta_smooth;
-        (void) z_beta_smooth;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta_smooth = in__.vector_constrain(K_smooth,lp__);
-        else
-            z_beta_smooth = in__.vector_constrain(K_smooth);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd_raw;
-        (void) smooth_sd_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ),lp__);
-        else
-            smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_b;
-        (void) z_b;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_b = in__.vector_constrain(q,lp__);
-        else
-            z_b = in__.vector_constrain(q);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_T;
-        (void) z_T;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_T = in__.vector_constrain(len_z_T,lp__);
-        else
-            z_T = in__.vector_constrain(len_z_T);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  rho;
-        (void) rho;  // dummy to suppress unused var warning
-        if (jacobian__)
-            rho = in__.vector_lub_constrain(0,1,len_rho,lp__);
-        else
-            rho = in__.vector_lub_constrain(0,1,len_rho);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  zeta;
-        (void) zeta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            zeta = in__.vector_lb_constrain(0,len_concentration,lp__);
-        else
-            zeta = in__.vector_lb_constrain(0,len_concentration);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.vector_lb_constrain(0,t,lp__);
-        else
-            tau = in__.vector_lb_constrain(0,t);
-
-        vector<T__> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = logical_gt(family,1);
-        aux_unscaled.reserve(dim_aux_unscaled_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            if (jacobian__)
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > noise;
-        size_t dim_noise_0__ = logical_eq(family,3);
-        noise.reserve(dim_noise_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_noise_0__; ++k_0__) {
-            if (jacobian__)
-                noise.push_back(in__.vector_lb_constrain(0,N,lp__));
-            else
-                noise.push_back(in__.vector_lb_constrain(0,N));
-        }
-
-
-        // transformed parameters
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::negative_infinity());
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, DUMMY_VAR__);
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, DUMMY_VAR__);
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, DUMMY_VAR__);
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, DUMMY_VAR__);
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 483;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 483;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 484;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 485;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 486;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 489;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 490;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 491;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 494;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 495;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 496;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 499;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 501;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 504;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 505;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 506;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 507;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 508;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 509;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 511;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 515;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 516;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 517;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 517;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 518;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 521;
-            if (as_bool((primitive_value(logical_gt(family,1)) && primitive_value((primitive_value(logical_eq(prior_dist_for_aux,0)) || primitive_value(logical_lte(prior_scale_for_aux,0))))))) {
-                current_statement_begin__ = 522;
-                stan::math::assign(aux, get_base1(aux_unscaled,1,"aux_unscaled",1));
-            } else if (as_bool(logical_gt(family,1))) {
-
-                current_statement_begin__ = 524;
-                stan::math::assign(aux, (prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)));
-                current_statement_begin__ = 525;
-                if (as_bool(logical_lte(prior_dist_for_aux,2))) {
-                    current_statement_begin__ = 526;
-                    stan::math::assign(aux, (aux + prior_mean_for_aux));
-                }
-            }
-            current_statement_begin__ = 529;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 530;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 532;
-                        stan::math::assign(theta_L, elt_multiply(scale,(logical_eq(family,1) ? stan::math::promote_scalar<T__>(tau) : stan::math::promote_scalar<T__>(multiply(tau,aux)) )));
-                        current_statement_begin__ = 533;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 533;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 534;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 536;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 537;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 541;
-                    if (as_bool(logical_eq(family,1))) {
-                        current_statement_begin__ = 542;
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    } else {
-                        current_statement_begin__ = 545;
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    }
-                    current_statement_begin__ = 547;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < K_smooth; ++i0__) {
-            if (stan::math::is_uninitialized(beta_smooth(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta_smooth" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++i0__) {
-            if (stan::math::is_uninitialized(smooth_sd(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: smooth_sd" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < q; ++i0__) {
-            if (stan::math::is_uninitialized(b(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: b" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < len_theta_L; ++i0__) {
-            if (stan::math::is_uninitialized(theta_L(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: theta_L" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 554;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 555;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 555;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 556;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 558;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 559;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 559;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 560;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 560;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 561;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 563;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 563;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 563;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 564;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 566;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 567;
-                    if (as_bool(logical_eq(link,1))) {
-                        current_statement_begin__ = 567;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        current_statement_begin__ = 568;
-                        stan::math::assign(eta, add(subtract(eta,min(eta)),get_base1(gamma,1,"gamma",1)));
-                    }
-                } else {
-
-                    current_statement_begin__ = 573;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 576;
-                if (as_bool(logical_eq(family,3))) {
-
-                    current_statement_begin__ = 577;
-                    if (as_bool(logical_eq(link,1))) {
-                        current_statement_begin__ = 577;
-                        stan::math::assign(eta, add(add(eta,log(aux)),log(get_base1(noise,1,"noise",1))));
-                    } else if (as_bool(logical_eq(link,2))) {
-                        current_statement_begin__ = 578;
-                        stan::math::assign(eta, elt_multiply(multiply(eta,aux),get_base1(noise,1,"noise",1)));
-                    } else {
-                        current_statement_begin__ = 579;
-                        stan::math::assign(eta, add(add(eta,sqrt(aux)),sqrt(get_base1(noise,1,"noise",1))));
-                    }
-                }
-                current_statement_begin__ = 583;
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    current_statement_begin__ = 584;
-                    if (as_bool(logical_neq(family,2))) {
-
-                        current_statement_begin__ = 585;
-                        if (as_bool(logical_eq(link,1))) {
-                            current_statement_begin__ = 585;
-                            lp_accum__.add(poisson_log_log(y,eta));
-                        } else {
-                            current_statement_begin__ = 586;
-                            lp_accum__.add(poisson_log(y,linkinv_count(eta,link, pstream__)));
-                        }
-                    } else {
-
-                        current_statement_begin__ = 589;
-                        if (as_bool(logical_eq(link,1))) {
-                            current_statement_begin__ = 589;
-                            lp_accum__.add(neg_binomial_2_log_log(y,eta,aux));
-                        } else {
-                            current_statement_begin__ = 590;
-                            lp_accum__.add(neg_binomial_2_log(y,linkinv_count(eta,link, pstream__),aux));
-                        }
-                    }
-                } else if (as_bool((primitive_value(logical_neq(family,2)) && primitive_value(logical_eq(prior_PD,0))))) {
-                    current_statement_begin__ = 594;
-                    lp_accum__.add(dot_product(weights,pw_pois(y,eta,link, pstream__)));
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-                    current_statement_begin__ = 596;
-                    lp_accum__.add(dot_product(weights,pw_nb(y,eta,aux,link, pstream__)));
-                }
-                current_statement_begin__ = 599;
-                if (as_bool((primitive_value((primitive_value(logical_gt(family,1)) && primitive_value(logical_gt(prior_dist_for_aux,0)))) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 602;
-                        if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                            current_statement_begin__ = 603;
-                            lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                        } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                            current_statement_begin__ = 605;
-                            lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                        } else {
-                            current_statement_begin__ = 607;
-                            lp_accum__.add(exponential_log(aux_unscaled,1));
-                        }
-                    }
-                }
-                current_statement_begin__ = 613;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 613;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    current_statement_begin__ = 614;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 617;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 618;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 619;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 620;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 621;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 625;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 626;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 627;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 628;
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 630;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        current_statement_begin__ = 631;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 632;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    current_statement_begin__ = 635;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 636;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    current_statement_begin__ = 639;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 640;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    current_statement_begin__ = 641;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    current_statement_begin__ = 644;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                current_statement_begin__ = 649;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 650;
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        current_statement_begin__ = 651;
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        current_statement_begin__ = 653;
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                current_statement_begin__ = 658;
-                if (as_bool(K_smooth)) {
-
-                    current_statement_begin__ = 659;
-                    lp_accum__.add(normal_log(z_beta_smooth,0,1));
-                    current_statement_begin__ = 660;
-                    if (as_bool(logical_gt(prior_dist_for_smooth,0))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            current_statement_begin__ = 662;
-                            if (as_bool(logical_eq(prior_dist_for_smooth,1))) {
-                                current_statement_begin__ = 663;
-                                lp_accum__.add((normal_log(smooth_sd_raw,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,2))) {
-                                current_statement_begin__ = 665;
-                                lp_accum__.add((student_t_log(smooth_sd_raw,prior_df_for_smooth,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_smooth,3))) {
-                                current_statement_begin__ = 667;
-                                lp_accum__.add(exponential_log(smooth_sd_raw,1));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 672;
-                if (as_bool(logical_eq(family,3))) {
-                    current_statement_begin__ = 672;
-                    lp_accum__.add(gamma_log(get_base1(noise,1,"noise",1),aux,1));
-                }
-                current_statement_begin__ = 674;
-                if (as_bool(logical_gt(t,0))) {
-                    current_statement_begin__ = 674;
-                    decov_lp(z_b,z_T,rho,zeta,tau,regularization,delta,shape,t,p, lp__, lp_accum__, pstream__);
-                }
-                current_statement_begin__ = 675;
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("z_beta_smooth");
-        names__.push_back("smooth_sd_raw");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("one_over_lambda");
-        names__.push_back("z_b");
-        names__.push_back("z_T");
-        names__.push_back("rho");
-        names__.push_back("zeta");
-        names__.push_back("tau");
-        names__.push_back("aux_unscaled");
-        names__.push_back("noise");
-        names__.push_back("aux");
-        names__.push_back("beta");
-        names__.push_back("beta_smooth");
-        names__.push_back("smooth_sd");
-        names__.push_back("b");
-        names__.push_back("theta_L");
-        names__.push_back("alpha");
-        names__.push_back("mean_PPD");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_z_T);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_rho);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_concentration);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(t);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_gt(family,1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(family,3));
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K_smooth);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(q);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(len_theta_L);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_count_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lb_constrain((logical_eq(link,1) ? stan::math::negative_infinity() : 0.0 )));
-        }
-        vector_d z_beta = in__.vector_constrain((logical_eq(prior_dist,7) ? sum(num_normals) : K ));
-        vector_d z_beta_smooth = in__.vector_constrain(K_smooth);
-        vector_d smooth_sd_raw = in__.vector_lb_constrain(0,(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector_d z_b = in__.vector_constrain(q);
-        vector_d z_T = in__.vector_constrain(len_z_T);
-        vector_d rho = in__.vector_lub_constrain(0,1,len_rho);
-        vector_d zeta = in__.vector_lb_constrain(0,len_concentration);
-        vector_d tau = in__.vector_lb_constrain(0,t);
-        vector<double> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = logical_gt(family,1);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> noise;
-        size_t dim_noise_0__ = logical_eq(family,3);
-        for (size_t k_0__ = 0; k_0__ < dim_noise_0__; ++k_0__) {
-            noise.push_back(in__.vector_lb_constrain(0,N));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(z_beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(z_b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_z_T; ++k_0__) {
-            vars__.push_back(z_T[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_rho; ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_concentration; ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < t; ++k_0__) {
-            vars__.push_back(tau[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_gt(family,1); ++k_0__) {
-            vars__.push_back(aux_unscaled[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < N; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < logical_eq(family,3); ++k_0__) {
-                vars__.push_back(noise[k_0__][k_1__]);
-            }
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,stan::math::negative_infinity());
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("beta_smooth", "K_smooth", K_smooth);
-        vector_d beta_smooth(static_cast<Eigen::VectorXd::Index>(K_smooth));
-        (void) beta_smooth;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta_smooth, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-        validate_non_negative_index("smooth_sd", "(logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,\"smooth_map\",1) : 0 )", (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ));
-        vector_d smooth_sd(static_cast<Eigen::VectorXd::Index>((logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 )));
-        (void) smooth_sd;  // dummy to suppress unused var warning
-
-        stan::math::initialize(smooth_sd, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(smooth_sd,DUMMY_VAR__);
-        validate_non_negative_index("b", "q", q);
-        vector_d b(static_cast<Eigen::VectorXd::Index>(q));
-        (void) b;  // dummy to suppress unused var warning
-
-        stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(b,DUMMY_VAR__);
-        validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-        vector_d theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-        (void) theta_L;  // dummy to suppress unused var warning
-
-        stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta_L,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 483;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 483;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 484;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 485;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 486;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 489;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 490;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 491;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 494;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 495;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 496;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 499;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 501;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 504;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 505;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 506;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 507;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 508;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 509;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 511;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-            current_statement_begin__ = 515;
-            if (as_bool(K_smooth)) {
-
-                current_statement_begin__ = 516;
-                stan::math::assign(smooth_sd, add(prior_mean_for_smooth,elt_multiply(prior_scale_for_smooth,smooth_sd_raw)));
-                current_statement_begin__ = 517;
-                if (as_bool((primitive_value(is_continuous) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 517;
-                    stan::math::assign(smooth_sd, multiply(smooth_sd,aux));
-                }
-                current_statement_begin__ = 518;
-                stan::math::assign(beta_smooth, elt_multiply(z_beta_smooth,stan::model::rvalue(smooth_sd, stan::model::cons_list(stan::model::index_multi(smooth_map), stan::model::nil_index_list()), "smooth_sd")));
-            }
-            current_statement_begin__ = 521;
-            if (as_bool((primitive_value(logical_gt(family,1)) && primitive_value((primitive_value(logical_eq(prior_dist_for_aux,0)) || primitive_value(logical_lte(prior_scale_for_aux,0))))))) {
-                current_statement_begin__ = 522;
-                stan::math::assign(aux, get_base1(aux_unscaled,1,"aux_unscaled",1));
-            } else if (as_bool(logical_gt(family,1))) {
-
-                current_statement_begin__ = 524;
-                stan::math::assign(aux, (prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)));
-                current_statement_begin__ = 525;
-                if (as_bool(logical_lte(prior_dist_for_aux,2))) {
-                    current_statement_begin__ = 526;
-                    stan::math::assign(aux, (aux + prior_mean_for_aux));
-                }
-            }
-            current_statement_begin__ = 529;
-            if (as_bool(logical_gt(t,0))) {
-
-                current_statement_begin__ = 530;
-                if (as_bool(logical_eq(special_case,1))) {
-                    {
-                        int start(0);
-                        (void) start;  // dummy to suppress unused var warning
-
-                        stan::math::fill(start, std::numeric_limits<int>::min());
-                        stan::math::assign(start,1);
-
-
-                        current_statement_begin__ = 532;
-                        stan::math::assign(theta_L, elt_multiply(scale,(logical_eq(family,1) ? stan::math::promote_scalar<double>(tau) : stan::math::promote_scalar<double>(multiply(tau,aux)) )));
-                        current_statement_begin__ = 533;
-                        if (as_bool(logical_eq(t,1))) {
-                            current_statement_begin__ = 533;
-                            stan::math::assign(b, multiply(get_base1(theta_L,1,"theta_L",1),z_b));
-                        } else {
-                            current_statement_begin__ = 534;
-                            for (int i = 1; i <= t; ++i) {
-                                {
-                                    int end(0);
-                                    (void) end;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(end, std::numeric_limits<int>::min());
-                                    stan::math::assign(end,((start + get_base1(l,i,"l",1)) - 1));
-
-
-                                    current_statement_begin__ = 536;
-                                    stan::model::assign(b, 
-                                                stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), 
-                                                multiply(get_base1(theta_L,i,"theta_L",1),stan::model::rvalue(z_b, stan::model::cons_list(stan::model::index_min_max(start, end), stan::model::nil_index_list()), "z_b")), 
-                                                "assigning variable b");
-                                    current_statement_begin__ = 537;
-                                    stan::math::assign(start, (end + 1));
-                                }
-                            }
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 541;
-                    if (as_bool(logical_eq(family,1))) {
-                        current_statement_begin__ = 542;
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,1.0,tau,scale,zeta,rho,z_T, pstream__));
-                    } else {
-                        current_statement_begin__ = 545;
-                        stan::math::assign(theta_L, make_theta_L(len_theta_L,p,aux,tau,scale,zeta,rho,z_T, pstream__));
-                    }
-                    current_statement_begin__ = 547;
-                    stan::math::assign(b, make_b(z_b,theta_L,p,l, pstream__));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K_smooth; ++k_0__) {
-            vars__.push_back(beta_smooth[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            vars__.push_back(smooth_sd[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < q; ++k_0__) {
-            vars__.push_back(b[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < len_theta_L; ++k_0__) {
-            vars__.push_back(theta_L[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-
-
-        try {
-            current_statement_begin__ = 680;
-            if (as_bool(logical_eq(has_intercept,1))) {
-
-                current_statement_begin__ = 681;
-                if (as_bool(dense_X)) {
-                    current_statement_begin__ = 681;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(xbar,beta)));
-                } else {
-                    current_statement_begin__ = 682;
-                    stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), get_base1(gamma,1,"gamma",1));
-                }
-            }
-            {
-                validate_non_negative_index("nu", "N", N);
-                vector_d nu(static_cast<Eigen::VectorXd::Index>(N));
-                (void) nu;  // dummy to suppress unused var warning
-
-                stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(nu,DUMMY_VAR__);
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 688;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 689;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 689;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 690;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 692;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 693;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 693;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 694;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 694;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 695;
-                if (as_bool(logical_gt(t,0))) {
-
-                    current_statement_begin__ = 697;
-                    if (as_bool(special_case)) {
-                        current_statement_begin__ = 697;
-                        for (int i = 1; i <= t; ++i) {
-                            current_statement_begin__ = 697;
-                            stan::math::assign(eta, add(eta,stan::model::rvalue(b, stan::model::cons_list(stan::model::index_multi(get_base1(V,i,"V",1)), stan::model::nil_index_list()), "b")));
-                        }
-                    } else {
-                        current_statement_begin__ = 698;
-                        stan::math::assign(eta, add(eta,csr_matrix_times_vector(N,q,w,v,u,b)));
-                    }
-                }
-                current_statement_begin__ = 700;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 701;
-                    if (as_bool(logical_eq(link,1))) {
-                        current_statement_begin__ = 701;
-                        stan::math::assign(eta, add(eta,get_base1(gamma,1,"gamma",1)));
-                    } else {
-                        {
-                            double shift(0.0);
-                            (void) shift;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(shift,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 704;
-                            stan::math::assign(shift, min(eta));
-                            current_statement_begin__ = 705;
-                            stan::math::assign(eta, add(subtract(eta,shift),get_base1(gamma,1,"gamma",1)));
-                            current_statement_begin__ = 706;
-                            stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(alpha,1,"alpha",1) - shift));
-                        }
-                    }
-                } else {
-
-                    current_statement_begin__ = 712;
-                    stan::math::assign(eta, add(eta,dot_product(xbar,beta)));
-                }
-                current_statement_begin__ = 715;
-                if (as_bool(logical_eq(family,3))) {
-
-                    current_statement_begin__ = 716;
-                    if (as_bool(logical_eq(link,1))) {
-                        current_statement_begin__ = 716;
-                        stan::math::assign(eta, add(add(eta,log(aux)),log(get_base1(noise,1,"noise",1))));
-                    } else if (as_bool(logical_eq(link,2))) {
-                        current_statement_begin__ = 717;
-                        stan::math::assign(eta, elt_multiply(multiply(eta,aux),get_base1(noise,1,"noise",1)));
-                    } else {
-                        current_statement_begin__ = 718;
-                        stan::math::assign(eta, add(add(eta,sqrt(aux)),sqrt(get_base1(noise,1,"noise",1))));
-                    }
-                }
-                current_statement_begin__ = 720;
-                stan::math::assign(nu, linkinv_count(eta,link, pstream__));
-                current_statement_begin__ = 721;
-                if (as_bool(logical_neq(family,2))) {
-                    current_statement_begin__ = 721;
-                    for (int n = 1; n <= N; ++n) {
-
-                        current_statement_begin__ = 722;
-                        if (as_bool(logical_lt(get_base1(nu,n,"nu",1),poisson_max))) {
-                            current_statement_begin__ = 722;
-                            stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(get_base1(nu,n,"nu",1), base_rng__)));
-                        } else {
-                            current_statement_begin__ = 723;
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(nu,n,"nu",1),sqrt(get_base1(nu,n,"nu",1)), base_rng__)));
-                        }
-                    }
-                } else {
-                    current_statement_begin__ = 725;
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double gamma_temp(0.0);
-                            (void) gamma_temp;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(gamma_temp, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(gamma_temp,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 727;
-                            if (as_bool(is_inf(aux))) {
-                                current_statement_begin__ = 727;
-                                stan::math::assign(gamma_temp, get_base1(nu,n,"nu",1));
-                            } else {
-                                current_statement_begin__ = 728;
-                                stan::math::assign(gamma_temp, gamma_rng(aux,(aux / get_base1(nu,n,"nu",1)), base_rng__));
-                            }
-                            current_statement_begin__ = 729;
-                            if (as_bool(logical_lt(gamma_temp,poisson_max))) {
-                                current_statement_begin__ = 730;
-                                stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(gamma_temp, base_rng__)));
-                            } else {
-                                current_statement_begin__ = 731;
-                                stan::math::assign(mean_PPD, (mean_PPD + normal_rng(gamma_temp,sqrt(gamma_temp), base_rng__)));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 733;
-                stan::math::assign(mean_PPD, (mean_PPD / N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        vars__.push_back(mean_PPD);
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_count";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_gt(family,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= N; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= logical_eq(family,3); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "noise" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(prior_dist,7) ? sum(num_normals) : K ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_z_T; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_T" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_rho; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_concentration; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= t; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "tau" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_gt(family,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= N; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= logical_eq(family,3); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "noise" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K_smooth; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta_smooth" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_gt(K_smooth,0) ? get_base1(smooth_map,K_smooth,"smooth_map",1) : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "smooth_sd" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= q; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "b" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= len_theta_L; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_L" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_lm_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_lm");
-    reader.add_event(133, 133, "end", "model_lm");
-    return reader;
-}
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type
-ll_mvn_ols_qr_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& theta,
-                     const Eigen::Matrix<T1__, Eigen::Dynamic,1>& b,
-                     const T2__& intercept,
-                     const T3__& ybar,
-                     const T4__& SSR,
-                     const T5__& sigma,
-                     const int& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 38;
-        lp_accum__.add((((-(0.5) * ((dot_self(subtract(theta,b)) + (N * square((intercept - ybar)))) + SSR)) / square(sigma)) - (N * (log(sigma) + 0.91893853320467267))));
-        current_statement_begin__ = 42;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_mvn_ols_qr_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T_lp__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& theta,
-                     const Eigen::Matrix<T1__, Eigen::Dynamic,1>& b,
-                     const T2__& intercept,
-                     const T3__& ybar,
-                     const T4__& SSR,
-                     const T5__& sigma,
-                     const int& N, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_mvn_ols_qr_lp(theta, b, intercept, ybar, SSR, sigma, N, lp__, lp_accum__, pstream__);
-    }
-};
-
-class model_lm : public prob_grad {
-private:
-    int has_intercept;
-    int prior_dist_for_intercept;
-    double prior_scale_for_intercept;
-    double prior_mean_for_intercept;
-    int prior_dist;
-    int prior_PD;
-    double eta;
-    int J;
-    vector<int> N;
-    int K;
-    vector<vector_d> xbarR_inv;
-    vector<double> ybar;
-    double center_y;
-    vector<double> s_Y;
-    vector<vector_d> Rb;
-    vector<double> SSR;
-    vector<matrix_d> R_inv;
-    double half_K;
-    vector<double> sqrt_inv_N;
-    vector<double> sqrt_Nm1;
-public:
-    model_lm(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_lm(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_lm_namespace::model_lm";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "eta", "double", context__.to_vec());
-        eta = double(0);
-        vals_r__ = context__.vals_r("eta");
-        pos__ = 0;
-        eta = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "J", "int", context__.to_vec());
-        J = int(0);
-        vals_i__ = context__.vals_i("J");
-        pos__ = 0;
-        J = vals_i__[pos__++];
-        validate_non_negative_index("N", "J", J);
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec(J));
-        validate_non_negative_index("N", "J", J);
-        N = std::vector<int>(J,int(0));
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        size_t N_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < N_limit_0__; ++i_0__) {
-            N[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbarR_inv", "J", J);
-        validate_non_negative_index("xbarR_inv", "K", K);
-        context__.validate_dims("data initialization", "xbarR_inv", "vector_d", context__.to_vec(J,K));
-        validate_non_negative_index("xbarR_inv", "J", J);
-        validate_non_negative_index("xbarR_inv", "K", K);
-        xbarR_inv = std::vector<vector_d>(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("xbarR_inv");
-        pos__ = 0;
-        size_t xbarR_inv_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbarR_inv_i_vec_lim__; ++i_vec__) {
-            size_t xbarR_inv_limit_0__ = J;
-            for (size_t i_0__ = 0; i_0__ < xbarR_inv_limit_0__; ++i_0__) {
-                xbarR_inv[i_0__][i_vec__] = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("ybar", "J", J);
-        context__.validate_dims("data initialization", "ybar", "double", context__.to_vec(J));
-        validate_non_negative_index("ybar", "J", J);
-        ybar = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("ybar");
-        pos__ = 0;
-        size_t ybar_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < ybar_limit_0__; ++i_0__) {
-            ybar[i_0__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "center_y", "double", context__.to_vec());
-        center_y = double(0);
-        vals_r__ = context__.vals_r("center_y");
-        pos__ = 0;
-        center_y = vals_r__[pos__++];
-        validate_non_negative_index("s_Y", "J", J);
-        context__.validate_dims("data initialization", "s_Y", "double", context__.to_vec(J));
-        validate_non_negative_index("s_Y", "J", J);
-        s_Y = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("s_Y");
-        pos__ = 0;
-        size_t s_Y_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < s_Y_limit_0__; ++i_0__) {
-            s_Y[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("Rb", "J", J);
-        validate_non_negative_index("Rb", "K", K);
-        context__.validate_dims("data initialization", "Rb", "vector_d", context__.to_vec(J,K));
-        validate_non_negative_index("Rb", "J", J);
-        validate_non_negative_index("Rb", "K", K);
-        Rb = std::vector<vector_d>(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("Rb");
-        pos__ = 0;
-        size_t Rb_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < Rb_i_vec_lim__; ++i_vec__) {
-            size_t Rb_limit_0__ = J;
-            for (size_t i_0__ = 0; i_0__ < Rb_limit_0__; ++i_0__) {
-                Rb[i_0__][i_vec__] = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("SSR", "J", J);
-        context__.validate_dims("data initialization", "SSR", "double", context__.to_vec(J));
-        validate_non_negative_index("SSR", "J", J);
-        SSR = std::vector<double>(J,double(0));
-        vals_r__ = context__.vals_r("SSR");
-        pos__ = 0;
-        size_t SSR_limit_0__ = J;
-        for (size_t i_0__ = 0; i_0__ < SSR_limit_0__; ++i_0__) {
-            SSR[i_0__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("R_inv", "J", J);
-        validate_non_negative_index("R_inv", "K", K);
-        validate_non_negative_index("R_inv", "K", K);
-        context__.validate_dims("data initialization", "R_inv", "matrix_d", context__.to_vec(J,K,K));
-        validate_non_negative_index("R_inv", "J", J);
-        validate_non_negative_index("R_inv", "K", K);
-        validate_non_negative_index("R_inv", "K", K);
-        R_inv = std::vector<matrix_d>(J,matrix_d(static_cast<Eigen::VectorXd::Index>(K),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("R_inv");
-        pos__ = 0;
-        size_t R_inv_m_mat_lim__ = K;
-        size_t R_inv_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < R_inv_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < R_inv_m_mat_lim__; ++m_mat__) {
-                size_t R_inv_limit_0__ = J;
-                for (size_t i_0__ = 0; i_0__ < R_inv_limit_0__; ++i_0__) {
-                    R_inv[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-
-        // validate, data variables
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,1);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,1);
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"eta",eta,0);
-        check_greater_or_equal(function__,"J",J,1);
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"N[k0__]",N[k0__],1);
-        }
-        check_greater_or_equal(function__,"K",K,1);
-        check_less_or_equal(function__,"K",K,min(N));
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"s_Y[k0__]",s_Y[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"SSR[k0__]",SSR[k0__],0);
-        }
-        // initialize data variables
-        half_K = double(0);
-        stan::math::fill(half_K,DUMMY_VAR__);
-        stan::math::assign(half_K,(0.5 * K));
-        validate_non_negative_index("sqrt_inv_N", "J", J);
-        sqrt_inv_N = std::vector<double>(J,double(0));
-        stan::math::fill(sqrt_inv_N,DUMMY_VAR__);
-        validate_non_negative_index("sqrt_Nm1", "J", J);
-        sqrt_Nm1 = std::vector<double>(J,double(0));
-        stan::math::fill(sqrt_Nm1,DUMMY_VAR__);
-
-        try {
-            current_statement_begin__ = 70;
-            for (int j = 1; j <= J; ++j) {
-
-                current_statement_begin__ = 71;
-                stan::math::assign(get_base1_lhs(sqrt_inv_N,j,"sqrt_inv_N",1), sqrt((1.0 / get_base1(N,j,"N",1))));
-                current_statement_begin__ = 72;
-                stan::math::assign(get_base1_lhs(sqrt_Nm1,j,"sqrt_Nm1",1), sqrt((get_base1(N,j,"N",1) - 1.0)));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("u", "K", K);
-        validate_non_negative_index("u", "J", J);
-        num_params_r__ += (K) * J;
-        validate_non_negative_index("z_alpha", "(J * has_intercept)", (J * has_intercept));
-        num_params_r__ += (J * has_intercept);
-        validate_non_negative_index("R2", "J", J);
-        num_params_r__ += J;
-        validate_non_negative_index("log_omega", "(J * (1 - prior_PD))", (J * (1 - prior_PD)));
-        num_params_r__ += (J * (1 - prior_PD));
-    }
-
-    ~model_lm() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("u")))
-            throw std::runtime_error("variable u missing");
-        vals_r__ = context__.vals_r("u");
-        pos__ = 0U;
-        validate_non_negative_index("u", "J", J);
-        validate_non_negative_index("u", "K", K);
-        context__.validate_dims("initialization", "u", "vector_d", context__.to_vec(J,K));
-        // generate_declaration u
-        std::vector<vector_d> u(J,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < J; ++i0__)
-                u[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            try {
-            writer__.unit_vector_unconstrain(u[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable u: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_alpha")))
-            throw std::runtime_error("variable z_alpha missing");
-        vals_r__ = context__.vals_r("z_alpha");
-        pos__ = 0U;
-        validate_non_negative_index("z_alpha", "(J * has_intercept)", (J * has_intercept));
-        context__.validate_dims("initialization", "z_alpha", "double", context__.to_vec((J * has_intercept)));
-        // generate_declaration z_alpha
-        std::vector<double> z_alpha((J * has_intercept),double(0));
-        for (int i0__ = 0U; i0__ < (J * has_intercept); ++i0__)
-            z_alpha[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (J * has_intercept); ++i0__)
-            try {
-            writer__.scalar_unconstrain(z_alpha[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_alpha: ") + e.what());
-        }
-
-        if (!(context__.contains_r("R2")))
-            throw std::runtime_error("variable R2 missing");
-        vals_r__ = context__.vals_r("R2");
-        pos__ = 0U;
-        validate_non_negative_index("R2", "J", J);
-        context__.validate_dims("initialization", "R2", "double", context__.to_vec(J));
-        // generate_declaration R2
-        std::vector<double> R2(J,double(0));
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            R2[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < J; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(0,1,R2[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable R2: ") + e.what());
-        }
-
-        if (!(context__.contains_r("log_omega")))
-            throw std::runtime_error("variable log_omega missing");
-        vals_r__ = context__.vals_r("log_omega");
-        pos__ = 0U;
-        validate_non_negative_index("log_omega", "(J * (1 - prior_PD))", (J * (1 - prior_PD)));
-        context__.validate_dims("initialization", "log_omega", "vector_d", context__.to_vec((J * (1 - prior_PD))));
-        // generate_declaration log_omega
-        vector_d log_omega(static_cast<Eigen::VectorXd::Index>((J * (1 - prior_PD))));
-        for (int j1__ = 0U; j1__ < (J * (1 - prior_PD)); ++j1__)
-            log_omega(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(log_omega);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable log_omega: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > u;
-        size_t dim_u_0__ = J;
-        u.reserve(dim_u_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_u_0__; ++k_0__) {
-            if (jacobian__)
-                u.push_back(in__.unit_vector_constrain(K,lp__));
-            else
-                u.push_back(in__.unit_vector_constrain(K));
-        }
-
-        vector<T__> z_alpha;
-        size_t dim_z_alpha_0__ = (J * has_intercept);
-        z_alpha.reserve(dim_z_alpha_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_z_alpha_0__; ++k_0__) {
-            if (jacobian__)
-                z_alpha.push_back(in__.scalar_constrain(lp__));
-            else
-                z_alpha.push_back(in__.scalar_constrain());
-        }
-
-        vector<T__> R2;
-        size_t dim_R2_0__ = J;
-        R2.reserve(dim_R2_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_R2_0__; ++k_0__) {
-            if (jacobian__)
-                R2.push_back(in__.scalar_lub_constrain(0,1,lp__));
-            else
-                R2.push_back(in__.scalar_lub_constrain(0,1));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  log_omega;
-        (void) log_omega;  // dummy to suppress unused var warning
-        if (jacobian__)
-            log_omega = in__.vector_constrain((J * (1 - prior_PD)),lp__);
-        else
-            log_omega = in__.vector_constrain((J * (1 - prior_PD)));
-
-
-        // transformed parameters
-        validate_non_negative_index("alpha", "(J * has_intercept)", (J * has_intercept));
-        vector<T__> alpha((J * has_intercept));
-        stan::math::initialize(alpha, DUMMY_VAR__);
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("theta", "K", K);
-        validate_non_negative_index("theta", "J", J);
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > theta(J, (Eigen::Matrix<T__,Eigen::Dynamic,1> (static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(theta, DUMMY_VAR__);
-        stan::math::fill(theta,DUMMY_VAR__);
-        validate_non_negative_index("sigma", "J", J);
-        vector<T__> sigma(J);
-        stan::math::initialize(sigma, DUMMY_VAR__);
-        stan::math::fill(sigma,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 85;
-            for (int j = 1; j <= J; ++j) {
-                {
-                    T__ Delta_y;
-                    (void) Delta_y;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Delta_y, DUMMY_VAR__);
-                    stan::math::fill(Delta_y,DUMMY_VAR__);
-                    stan::math::assign(Delta_y,(logical_eq(prior_PD,0) ? stan::math::promote_scalar<T__>((get_base1(s_Y,j,"s_Y",1) * exp(get_base1(log_omega,j,"log_omega",1)))) : stan::math::promote_scalar<T__>(1) ));
-
-
-                    current_statement_begin__ = 90;
-                    if (as_bool(logical_gt(K,1))) {
-                        current_statement_begin__ = 90;
-                        stan::math::assign(get_base1_lhs(theta,j,"theta",1), multiply(multiply(multiply(get_base1(u,j,"u",1),sqrt(get_base1(R2,j,"R2",1))),get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)),Delta_y));
-                    } else {
-                        current_statement_begin__ = 91;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(theta,j,"theta",1),1,"theta",2), (((get_base1(get_base1(u,j,"u",1),1,"u",2) * sqrt(get_base1(R2,j,"R2",1))) * get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)) * Delta_y));
-                    }
-                    current_statement_begin__ = 93;
-                    stan::math::assign(get_base1_lhs(sigma,j,"sigma",1), (Delta_y * sqrt((1 - get_base1(R2,j,"R2",1)))));
-                    current_statement_begin__ = 95;
-                    if (as_bool(logical_eq(has_intercept,1))) {
-
-                        current_statement_begin__ = 96;
-                        if (as_bool(logical_eq(prior_dist_for_intercept,0))) {
-                            current_statement_begin__ = 97;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), get_base1(z_alpha,j,"z_alpha",1));
-                        } else if (as_bool(logical_eq(prior_scale_for_intercept,0))) {
-                            current_statement_begin__ = 99;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), (((get_base1(z_alpha,j,"z_alpha",1) * Delta_y) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)) + prior_mean_for_intercept));
-                        } else {
-                            current_statement_begin__ = 101;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), ((get_base1(z_alpha,j,"z_alpha",1) * prior_scale_for_intercept) + prior_mean_for_intercept));
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < (J * has_intercept); ++i0__) {
-            if (stan::math::is_uninitialized(alpha[i0__])) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: alpha" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < J; ++i0__) {
-            for (int i1__ = 0; i1__ < K; ++i1__) {
-                if (stan::math::is_uninitialized(theta[i0__](i1__))) {
-                    std::stringstream msg__;
-                    msg__ << "Undefined transformed parameter: theta" << '[' << i0__ << ']' << '[' << i1__ << ']';
-                    throw std::runtime_error(msg__.str());
-                }
-            }
-        }
-        for (int i0__ = 0; i0__ < J; ++i0__) {
-            if (stan::math::is_uninitialized(sigma[i0__])) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: sigma" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"sigma[k0__]",sigma[k0__],0);
-        }
-
-        // model body
-        try {
-
-            current_statement_begin__ = 107;
-            for (int j = 1; j <= J; ++j) {
-
-                current_statement_begin__ = 108;
-                if (as_bool(logical_eq(prior_PD,0))) {
-                    {
-                        T__ dummy;
-                        (void) dummy;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(dummy, DUMMY_VAR__);
-                        stan::math::fill(dummy,DUMMY_VAR__);
-                        T__ shift;
-                        (void) shift;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shift, DUMMY_VAR__);
-                        stan::math::fill(shift,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 111;
-                        stan::math::assign(shift, dot_product(get_base1(xbarR_inv,j,"xbarR_inv",1),get_base1(theta,j,"theta",1)));
-                        current_statement_begin__ = 112;
-                        stan::math::assign(dummy, ll_mvn_ols_qr_lp(get_base1(theta,j,"theta",1),get_base1(Rb,j,"Rb",1),(logical_eq(has_intercept,1) ? stan::math::promote_scalar<T__>((get_base1(alpha,j,"alpha",1) + shift)) : stan::math::promote_scalar<T__>(shift) ),get_base1(ybar,j,"ybar",1),get_base1(SSR,j,"SSR",1),get_base1(sigma,j,"sigma",1),get_base1(N,j,"N",1), lp__, lp_accum__, pstream__));
-                    }
-                }
-            }
-            current_statement_begin__ = 118;
-            if (as_bool((primitive_value(logical_eq(has_intercept,1)) && primitive_value(logical_gt(prior_dist_for_intercept,0))))) {
-                current_statement_begin__ = 119;
-                lp_accum__.add(normal_log(z_alpha,0,1));
-            }
-            current_statement_begin__ = 120;
-            if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 120;
-                lp_accum__.add(beta_log(R2,half_K,eta));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("u");
-        names__.push_back("z_alpha");
-        names__.push_back("R2");
-        names__.push_back("log_omega");
-        names__.push_back("alpha");
-        names__.push_back("theta");
-        names__.push_back("sigma");
-        names__.push_back("mean_PPD");
-        names__.push_back("beta");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * has_intercept));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * (1 - prior_PD)));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J * has_intercept));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(J);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_lm_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<vector_d> u;
-        size_t dim_u_0__ = J;
-        for (size_t k_0__ = 0; k_0__ < dim_u_0__; ++k_0__) {
-            u.push_back(in__.unit_vector_constrain(K));
-        }
-        vector<double> z_alpha;
-        size_t dim_z_alpha_0__ = (J * has_intercept);
-        for (size_t k_0__ = 0; k_0__ < dim_z_alpha_0__; ++k_0__) {
-            z_alpha.push_back(in__.scalar_constrain());
-        }
-        vector<double> R2;
-        size_t dim_R2_0__ = J;
-        for (size_t k_0__ = 0; k_0__ < dim_R2_0__; ++k_0__) {
-            R2.push_back(in__.scalar_lub_constrain(0,1));
-        }
-        vector_d log_omega = in__.vector_constrain((J * (1 - prior_PD)));
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(u[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < (J * has_intercept); ++k_0__) {
-            vars__.push_back(z_alpha[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(R2[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J * (1 - prior_PD)); ++k_0__) {
-            vars__.push_back(log_omega[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("alpha", "(J * has_intercept)", (J * has_intercept));
-        vector<double> alpha((J * has_intercept), 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-        validate_non_negative_index("theta", "K", K);
-        validate_non_negative_index("theta", "J", J);
-        vector<vector_d> theta(J, (vector_d(static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(theta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(theta,DUMMY_VAR__);
-        validate_non_negative_index("sigma", "J", J);
-        vector<double> sigma(J, 0.0);
-        stan::math::initialize(sigma, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(sigma,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 85;
-            for (int j = 1; j <= J; ++j) {
-                {
-                    double Delta_y(0.0);
-                    (void) Delta_y;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(Delta_y, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(Delta_y,DUMMY_VAR__);
-                    stan::math::assign(Delta_y,(logical_eq(prior_PD,0) ? stan::math::promote_scalar<double>((get_base1(s_Y,j,"s_Y",1) * exp(get_base1(log_omega,j,"log_omega",1)))) : stan::math::promote_scalar<double>(1) ));
-
-
-                    current_statement_begin__ = 90;
-                    if (as_bool(logical_gt(K,1))) {
-                        current_statement_begin__ = 90;
-                        stan::math::assign(get_base1_lhs(theta,j,"theta",1), multiply(multiply(multiply(get_base1(u,j,"u",1),sqrt(get_base1(R2,j,"R2",1))),get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)),Delta_y));
-                    } else {
-                        current_statement_begin__ = 91;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(theta,j,"theta",1),1,"theta",2), (((get_base1(get_base1(u,j,"u",1),1,"u",2) * sqrt(get_base1(R2,j,"R2",1))) * get_base1(sqrt_Nm1,j,"sqrt_Nm1",1)) * Delta_y));
-                    }
-                    current_statement_begin__ = 93;
-                    stan::math::assign(get_base1_lhs(sigma,j,"sigma",1), (Delta_y * sqrt((1 - get_base1(R2,j,"R2",1)))));
-                    current_statement_begin__ = 95;
-                    if (as_bool(logical_eq(has_intercept,1))) {
-
-                        current_statement_begin__ = 96;
-                        if (as_bool(logical_eq(prior_dist_for_intercept,0))) {
-                            current_statement_begin__ = 97;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), get_base1(z_alpha,j,"z_alpha",1));
-                        } else if (as_bool(logical_eq(prior_scale_for_intercept,0))) {
-                            current_statement_begin__ = 99;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), (((get_base1(z_alpha,j,"z_alpha",1) * Delta_y) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)) + prior_mean_for_intercept));
-                        } else {
-                            current_statement_begin__ = 101;
-                            stan::math::assign(get_base1_lhs(alpha,j,"alpha",1), ((get_base1(z_alpha,j,"z_alpha",1) * prior_scale_for_intercept) + prior_mean_for_intercept));
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int k0__ = 0; k0__ < J; ++k0__) {
-            check_greater_or_equal(function__,"sigma[k0__]",sigma[k0__],0);
-        }
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < (J * has_intercept); ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(theta[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(sigma[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("mean_PPD", "J", J);
-        vector<double> mean_PPD(J, 0.0);
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        validate_non_negative_index("beta", "K", K);
-        validate_non_negative_index("beta", "J", J);
-        vector<vector_d> beta(J, (vector_d(static_cast<Eigen::VectorXd::Index>(K))));
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 126;
-            for (int j = 1; j <= J; ++j) {
-                {
-                    double shift(0.0);
-                    (void) shift;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(shift, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(shift,DUMMY_VAR__);
-
-
-                    current_statement_begin__ = 128;
-                    stan::math::assign(shift, dot_product(get_base1(xbarR_inv,j,"xbarR_inv",1),get_base1(theta,j,"theta",1)));
-                    current_statement_begin__ = 129;
-                    stan::math::assign(get_base1_lhs(mean_PPD,j,"mean_PPD",1), normal_rng((logical_eq(has_intercept,1) ? stan::math::promote_scalar<double>((get_base1(alpha,j,"alpha",1) + shift)) : stan::math::promote_scalar<double>(shift) ),(get_base1(sigma,j,"sigma",1) * get_base1(sqrt_inv_N,j,"sqrt_inv_N",1)), base_rng__));
-                    current_statement_begin__ = 131;
-                    stan::math::assign(get_base1_lhs(beta,j,"beta",1), multiply(get_base1(R_inv,j,"R_inv",1),get_base1(theta,j,"theta",1)));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(mean_PPD[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-                vars__.push_back(beta[k_0__][k_1__]);
-            }
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_lm";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "u" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "R2" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J * (1 - prior_PD)); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "log_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "theta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "sigma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "beta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "u" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "R2" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J * (1 - prior_PD)); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "log_omega" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= (J * has_intercept); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "theta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "sigma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "beta" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_polr_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_polr");
-    reader.add_event(313, 313, "end", "model_polr");
-    return reader;
-}
-
-template <typename T0__>
-typename boost::math::tools::promote_args<T0__>::type
-CDF_polr(const T0__& x,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 34;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 34;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(x));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 35;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(x));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 36;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(gumbel_cdf(x,0,1));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 37;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(x));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 38;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(cauchy_cdf(x,0,1));
-        } else {
-            current_statement_begin__ = 39;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 39;
-        current_statement_begin__ = 40;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CDF_polr_functor__ {
-    template <typename T0__>
-        typename boost::math::tools::promote_args<T0__>::type
-    operator()(const T0__& x,
-             const int& link, std::ostream* pstream__) const {
-        return CDF_polr(x, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__, T4__>::type, Eigen::Dynamic,1>
-pw_polr(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const Eigen::Matrix<T2__, Eigen::Dynamic,1>& cutpoints,
-            const int& link,
-            const T4__& alpha, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            int J(0);
-            (void) J;  // dummy to suppress unused var warning
-
-            stan::math::fill(J, std::numeric_limits<int>::min());
-            stan::math::assign(J,(rows(cutpoints) + 1));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 57;
-            if (as_bool((primitive_value(logical_lt(link,1)) || primitive_value(logical_gt(link,5))))) {
-                current_statement_begin__ = 58;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 58;
-            current_statement_begin__ = 60;
-            if (as_bool(logical_eq(alpha,1))) {
-                current_statement_begin__ = 60;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 61;
-                    if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                        current_statement_begin__ = 61;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                    } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                        current_statement_begin__ = 62;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (1 - CDF_polr((get_base1(cutpoints,(J - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__)));
-                    } else {
-                        current_statement_begin__ = 63;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (CDF_polr((get_base1(cutpoints,get_base1(y,n,"y",1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__) - CDF_polr((get_base1(cutpoints,(get_base1(y,n,"y",1) - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 66;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 67;
-                    if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                        current_statement_begin__ = 67;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), pow(CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__),alpha));
-                    } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                        current_statement_begin__ = 68;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), (1 - pow(CDF_polr((get_base1(cutpoints,(J - 1),"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__),alpha)));
-                    } else {
-                        current_statement_begin__ = 69;
-                        std::stringstream errmsg_stream__;
-                        errmsg_stream__ << "alpha not allowed with more than 2 outcome categories";
-                        throw std::domain_error(errmsg_stream__.str());
-                    }
-                }
-            }
-            current_statement_begin__ = 71;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(log(ll));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_polr_functor__ {
-    template <typename T1__, typename T2__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__, T4__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const Eigen::Matrix<T2__, Eigen::Dynamic,1>& cutpoints,
-            const int& link,
-            const T4__& alpha, std::ostream* pstream__) const {
-        return pw_polr(y, eta, cutpoints, link, alpha, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_cutpoints(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& probabilities,
-                   const T1__& scale,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int C(0);
-            (void) C;  // dummy to suppress unused var warning
-
-            stan::math::fill(C, std::numeric_limits<int>::min());
-            stan::math::assign(C,(rows(probabilities) - 1));
-            validate_non_negative_index("cutpoints", "C", C);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  cutpoints(static_cast<Eigen::VectorXd::Index>(C));
-            (void) cutpoints;  // dummy to suppress unused var warning
-
-            stan::math::initialize(cutpoints, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(cutpoints,DUMMY_VAR__);
-            fun_scalar_t__ running_sum;
-            (void) running_sum;  // dummy to suppress unused var warning
-
-            stan::math::initialize(running_sum, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(running_sum,DUMMY_VAR__);
-            stan::math::assign(running_sum,0);
-
-
-            current_statement_begin__ = 88;
-            if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 88;
-                for (int c = 1; c <= C; ++c) {
-
-                    current_statement_begin__ = 89;
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    current_statement_begin__ = 90;
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), logit(running_sum));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 92;
-                for (int c = 1; c <= C; ++c) {
-
-                    current_statement_begin__ = 93;
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    current_statement_begin__ = 94;
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), inv_Phi(running_sum));
-                }
-            } else if (as_bool(logical_eq(link,3))) {
-                current_statement_begin__ = 96;
-                for (int c = 1; c <= C; ++c) {
-
-                    current_statement_begin__ = 97;
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    current_statement_begin__ = 98;
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), -(log(-(log(running_sum)))));
-                }
-            } else if (as_bool(logical_eq(link,4))) {
-                current_statement_begin__ = 100;
-                for (int c = 1; c <= C; ++c) {
-
-                    current_statement_begin__ = 101;
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    current_statement_begin__ = 102;
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), log(-(log1m(running_sum))));
-                }
-            } else if (as_bool(logical_eq(link,5))) {
-                current_statement_begin__ = 104;
-                for (int c = 1; c <= C; ++c) {
-
-                    current_statement_begin__ = 105;
-                    stan::math::assign(running_sum, (running_sum + get_base1(probabilities,c,"probabilities",1)));
-                    current_statement_begin__ = 106;
-                    stan::math::assign(get_base1_lhs(cutpoints,c,"cutpoints",1), tan((stan::math::pi() * (running_sum - 0.5))));
-                }
-            } else {
-                current_statement_begin__ = 108;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 108;
-            current_statement_begin__ = 109;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(scale,cutpoints));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_cutpoints_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& probabilities,
-                   const T1__& scale,
-                   const int& link, std::ostream* pstream__) const {
-        return make_cutpoints(probabilities, scale, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__, T2__>::type
-draw_ystar_rng(const T0__& lower,
-                   const T1__& upper,
-                   const T2__& eta,
-                   const int& link, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int iter(0);
-            (void) iter;  // dummy to suppress unused var warning
-
-            stan::math::fill(iter, std::numeric_limits<int>::min());
-            stan::math::assign(iter,0);
-            fun_scalar_t__ ystar;
-            (void) ystar;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ystar, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ystar,DUMMY_VAR__);
-            stan::math::assign(ystar,stan::math::not_a_number());
-
-
-            current_statement_begin__ = 124;
-            if (as_bool(logical_gte(lower,upper))) {
-                current_statement_begin__ = 125;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "lower must be less than upper";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 125;
-            current_statement_begin__ = 129;
-            if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 129;
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    current_statement_begin__ = 130;
-                    stan::math::assign(ystar, logistic_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 131;
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    current_statement_begin__ = 132;
-                    stan::math::assign(ystar, normal_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,3))) {
-                current_statement_begin__ = 133;
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    current_statement_begin__ = 134;
-                    stan::math::assign(ystar, gumbel_rng(eta,1, base_rng__));
-                }
-            } else if (as_bool(logical_eq(link,4))) {
-                current_statement_begin__ = 135;
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    current_statement_begin__ = 136;
-                    stan::math::assign(ystar, log(-(log1m(uniform_rng(0,1, base_rng__)))));
-                }
-            } else if (as_bool(logical_eq(link,5))) {
-                current_statement_begin__ = 137;
-                while (as_bool(logical_negation((primitive_value(logical_gt(ystar,lower)) && primitive_value(logical_lt(ystar,upper)))))) {
-                    current_statement_begin__ = 138;
-                    stan::math::assign(ystar, cauchy_rng(eta,1, base_rng__));
-                }
-            } else {
-                current_statement_begin__ = 139;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 139;
-            current_statement_begin__ = 140;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ystar);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct draw_ystar_rng_functor__ {
-    template <typename T0__, typename T1__, typename T2__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__>::type
-    operator()(const T0__& lower,
-                   const T1__& upper,
-                   const T2__& eta,
-                   const int& link, RNG& base_rng__, std::ostream* pstream__) const {
-        return draw_ystar_rng(lower, upper, eta, link, base_rng__, pstream__);
-    }
-};
-
-class model_polr : public prob_grad {
-private:
-    int N;
-    int K;
-    vector_d xbar;
-    int dense_X;
-    vector<matrix_d> X;
-    int nnz_X;
-    vector_d w_X;
-    vector<int> v_X;
-    vector<int> u_X;
-    int K_smooth;
-    matrix_d S;
-    vector<int> smooth_map;
-    int J;
-    vector<int> y;
-    int prior_PD;
-    int has_intercept;
-    int family;
-    int link;
-    int prior_dist;
-    int prior_dist_for_intercept;
-    int prior_dist_for_aux;
-    int prior_dist_for_smooth;
-    int has_weights;
-    vector_d weights;
-    int has_offset;
-    vector_d offset;
-    double regularization;
-    vector_d prior_counts;
-    int is_skewed;
-    double shape;
-    double rate;
-    int do_residuals;
-    double half_K;
-    double sqrt_Nm1;
-    int is_constant;
-    vector_d beta_smooth;
-public:
-    model_polr(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_polr(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_polr_namespace::model_polr";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "dense_X", "int", context__.to_vec());
-        dense_X = int(0);
-        vals_i__ = context__.vals_i("dense_X");
-        pos__ = 0;
-        dense_X = vals_i__[pos__++];
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(dense_X,N,K));
-        validate_non_negative_index("X", "dense_X", dense_X);
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = std::vector<matrix_d>(dense_X,matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K)));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                size_t X_limit_0__ = dense_X;
-                for (size_t i_0__ = 0; i_0__ < X_limit_0__; ++i_0__) {
-                    X[i_0__](m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-            }
-        }
-        context__.validate_dims("data initialization", "nnz_X", "int", context__.to_vec());
-        nnz_X = int(0);
-        vals_i__ = context__.vals_i("nnz_X");
-        pos__ = 0;
-        nnz_X = vals_i__[pos__++];
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "w_X", "vector_d", context__.to_vec(nnz_X));
-        validate_non_negative_index("w_X", "nnz_X", nnz_X);
-        w_X = vector_d(static_cast<Eigen::VectorXd::Index>(nnz_X));
-        vals_r__ = context__.vals_r("w_X");
-        pos__ = 0;
-        size_t w_X_i_vec_lim__ = nnz_X;
-        for (size_t i_vec__ = 0; i_vec__ < w_X_i_vec_lim__; ++i_vec__) {
-            w_X[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        context__.validate_dims("data initialization", "v_X", "int", context__.to_vec(nnz_X));
-        validate_non_negative_index("v_X", "nnz_X", nnz_X);
-        v_X = std::vector<int>(nnz_X,int(0));
-        vals_i__ = context__.vals_i("v_X");
-        pos__ = 0;
-        size_t v_X_limit_0__ = nnz_X;
-        for (size_t i_0__ = 0; i_0__ < v_X_limit_0__; ++i_0__) {
-            v_X[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        context__.validate_dims("data initialization", "u_X", "int", context__.to_vec((dense_X ? 0 : (N + 1) )));
-        validate_non_negative_index("u_X", "(dense_X ? 0 : (N + 1) )", (dense_X ? 0 : (N + 1) ));
-        u_X = std::vector<int>((dense_X ? 0 : (N + 1) ),int(0));
-        vals_i__ = context__.vals_i("u_X");
-        pos__ = 0;
-        size_t u_X_limit_0__ = (dense_X ? 0 : (N + 1) );
-        for (size_t i_0__ = 0; i_0__ < u_X_limit_0__; ++i_0__) {
-            u_X[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "K_smooth", "int", context__.to_vec());
-        K_smooth = int(0);
-        vals_i__ = context__.vals_i("K_smooth");
-        pos__ = 0;
-        K_smooth = vals_i__[pos__++];
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "S", "matrix_d", context__.to_vec(N,K_smooth));
-        validate_non_negative_index("S", "N", N);
-        validate_non_negative_index("S", "K_smooth", K_smooth);
-        S = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K_smooth));
-        vals_r__ = context__.vals_r("S");
-        pos__ = 0;
-        size_t S_m_mat_lim__ = N;
-        size_t S_n_mat_lim__ = K_smooth;
-        for (size_t n_mat__ = 0; n_mat__ < S_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < S_m_mat_lim__; ++m_mat__) {
-                S(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        context__.validate_dims("data initialization", "smooth_map", "int", context__.to_vec(K_smooth));
-        validate_non_negative_index("smooth_map", "K_smooth", K_smooth);
-        smooth_map = std::vector<int>(K_smooth,int(0));
-        vals_i__ = context__.vals_i("smooth_map");
-        pos__ = 0;
-        size_t smooth_map_limit_0__ = K_smooth;
-        for (size_t i_0__ = 0; i_0__ < smooth_map_limit_0__; ++i_0__) {
-            smooth_map[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "J", "int", context__.to_vec());
-        J = int(0);
-        vals_i__ = context__.vals_i("J");
-        pos__ = 0;
-        J = vals_i__[pos__++];
-        validate_non_negative_index("y", "N", N);
-        context__.validate_dims("data initialization", "y", "int", context__.to_vec(N));
-        validate_non_negative_index("y", "N", N);
-        y = std::vector<int>(N,int(0));
-        vals_i__ = context__.vals_i("y");
-        pos__ = 0;
-        size_t y_limit_0__ = N;
-        for (size_t i_0__ = 0; i_0__ < y_limit_0__; ++i_0__) {
-            y[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_PD", "int", context__.to_vec());
-        prior_PD = int(0);
-        vals_i__ = context__.vals_i("prior_PD");
-        pos__ = 0;
-        prior_PD = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_smooth", "int", context__.to_vec());
-        prior_dist_for_smooth = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_smooth");
-        pos__ = 0;
-        prior_dist_for_smooth = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_weights", "int", context__.to_vec());
-        has_weights = int(0);
-        vals_i__ = context__.vals_i("has_weights");
-        pos__ = 0;
-        has_weights = vals_i__[pos__++];
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        context__.validate_dims("data initialization", "weights", "vector_d", context__.to_vec((has_weights ? N : 0 )));
-        validate_non_negative_index("weights", "(has_weights ? N : 0 )", (has_weights ? N : 0 ));
-        weights = vector_d(static_cast<Eigen::VectorXd::Index>((has_weights ? N : 0 )));
-        vals_r__ = context__.vals_r("weights");
-        pos__ = 0;
-        size_t weights_i_vec_lim__ = (has_weights ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < weights_i_vec_lim__; ++i_vec__) {
-            weights[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "has_offset", "int", context__.to_vec());
-        has_offset = int(0);
-        vals_i__ = context__.vals_i("has_offset");
-        pos__ = 0;
-        has_offset = vals_i__[pos__++];
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        context__.validate_dims("data initialization", "offset", "vector_d", context__.to_vec((has_offset ? N : 0 )));
-        validate_non_negative_index("offset", "(has_offset ? N : 0 )", (has_offset ? N : 0 ));
-        offset = vector_d(static_cast<Eigen::VectorXd::Index>((has_offset ? N : 0 )));
-        vals_r__ = context__.vals_r("offset");
-        pos__ = 0;
-        size_t offset_i_vec_lim__ = (has_offset ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < offset_i_vec_lim__; ++i_vec__) {
-            offset[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "regularization", "double", context__.to_vec());
-        regularization = double(0);
-        vals_r__ = context__.vals_r("regularization");
-        pos__ = 0;
-        regularization = vals_r__[pos__++];
-        validate_non_negative_index("prior_counts", "J", J);
-        context__.validate_dims("data initialization", "prior_counts", "vector_d", context__.to_vec(J));
-        validate_non_negative_index("prior_counts", "J", J);
-        prior_counts = vector_d(static_cast<Eigen::VectorXd::Index>(J));
-        vals_r__ = context__.vals_r("prior_counts");
-        pos__ = 0;
-        size_t prior_counts_i_vec_lim__ = J;
-        for (size_t i_vec__ = 0; i_vec__ < prior_counts_i_vec_lim__; ++i_vec__) {
-            prior_counts[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "is_skewed", "int", context__.to_vec());
-        is_skewed = int(0);
-        vals_i__ = context__.vals_i("is_skewed");
-        pos__ = 0;
-        is_skewed = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "shape", "double", context__.to_vec());
-        shape = double(0);
-        vals_r__ = context__.vals_r("shape");
-        pos__ = 0;
-        shape = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "rate", "double", context__.to_vec());
-        rate = double(0);
-        vals_r__ = context__.vals_r("rate");
-        pos__ = 0;
-        rate = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "do_residuals", "int", context__.to_vec());
-        do_residuals = int(0);
-        vals_i__ = context__.vals_i("do_residuals");
-        pos__ = 0;
-        do_residuals = vals_i__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"dense_X",dense_X,0);
-        check_less_or_equal(function__,"dense_X",dense_X,1);
-        check_greater_or_equal(function__,"nnz_X",nnz_X,0);
-        for (int k0__ = 0; k0__ < nnz_X; ++k0__) {
-            check_greater_or_equal(function__,"v_X[k0__]",v_X[k0__],0);
-        }
-        for (int k0__ = 0; k0__ < (dense_X ? 0 : (N + 1) ); ++k0__) {
-            check_greater_or_equal(function__,"u_X[k0__]",u_X[k0__],0);
-        }
-        check_greater_or_equal(function__,"K_smooth",K_smooth,0);
-        for (int k0__ = 0; k0__ < K_smooth; ++k0__) {
-            check_greater_or_equal(function__,"smooth_map[k0__]",smooth_map[k0__],1);
-        }
-        check_greater_or_equal(function__,"J",J,2);
-        for (int k0__ = 0; k0__ < N; ++k0__) {
-            check_greater_or_equal(function__,"y[k0__]",y[k0__],1);
-            check_less_or_equal(function__,"y[k0__]",y[k0__],J);
-        }
-        check_greater_or_equal(function__,"prior_PD",prior_PD,0);
-        check_less_or_equal(function__,"prior_PD",prior_PD,1);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        check_greater_or_equal(function__,"family",family,1);
-        check_greater_or_equal(function__,"link",link,1);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_less_or_equal(function__,"prior_dist",prior_dist,7);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_less_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,2);
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,0);
-        check_less_or_equal(function__,"prior_dist_for_smooth",prior_dist_for_smooth,3);
-        check_greater_or_equal(function__,"has_weights",has_weights,0);
-        check_less_or_equal(function__,"has_weights",has_weights,1);
-        check_greater_or_equal(function__,"has_offset",has_offset,0);
-        check_less_or_equal(function__,"has_offset",has_offset,1);
-        check_greater_or_equal(function__,"regularization",regularization,0);
-        check_greater_or_equal(function__,"prior_counts",prior_counts,0);
-        check_greater_or_equal(function__,"is_skewed",is_skewed,0);
-        check_less_or_equal(function__,"is_skewed",is_skewed,1);
-        check_greater_or_equal(function__,"shape",shape,0);
-        check_greater_or_equal(function__,"rate",rate,0);
-        check_greater_or_equal(function__,"do_residuals",do_residuals,0);
-        check_less_or_equal(function__,"do_residuals",do_residuals,1);
-        // initialize data variables
-        half_K = double(0);
-        stan::math::fill(half_K,DUMMY_VAR__);
-        stan::math::assign(half_K,(0.5 * K));
-        sqrt_Nm1 = double(0);
-        stan::math::fill(sqrt_Nm1,DUMMY_VAR__);
-        stan::math::assign(sqrt_Nm1,sqrt((N - 1.0)));
-        is_constant = int(0);
-        stan::math::fill(is_constant, std::numeric_limits<int>::min());
-        stan::math::assign(is_constant,1);
-        validate_non_negative_index("beta_smooth", "0", 0);
-        beta_smooth = vector_d(static_cast<Eigen::VectorXd::Index>(0));
-        stan::math::fill(beta_smooth,DUMMY_VAR__);
-
-        try {
-            current_statement_begin__ = 211;
-            for (int j = 1; j <= J; ++j) {
-                current_statement_begin__ = 211;
-                if (as_bool(logical_neq(get_base1(prior_counts,j,"prior_counts",1),1))) {
-                    current_statement_begin__ = 211;
-                    stan::math::assign(is_constant, 0);
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        check_greater_or_equal(function__,"half_K",half_K,0);
-        check_greater_or_equal(function__,"sqrt_Nm1",sqrt_Nm1,0);
-        check_greater_or_equal(function__,"is_constant",is_constant,0);
-        check_less_or_equal(function__,"is_constant",is_constant,1);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("pi", "J", J);
-        num_params_r__ += (J - 1);
-        validate_non_negative_index("u", "K", K);
-        num_params_r__ += (K);
-        ++num_params_r__;
-        validate_non_negative_index("alpha", "is_skewed", is_skewed);
-        num_params_r__ += is_skewed;
-    }
-
-    ~model_polr() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("pi")))
-            throw std::runtime_error("variable pi missing");
-        vals_r__ = context__.vals_r("pi");
-        pos__ = 0U;
-        validate_non_negative_index("pi", "J", J);
-        context__.validate_dims("initialization", "pi", "vector_d", context__.to_vec(J));
-        // generate_declaration pi
-        vector_d pi(static_cast<Eigen::VectorXd::Index>(J));
-        for (int j1__ = 0U; j1__ < J; ++j1__)
-            pi(j1__) = vals_r__[pos__++];
-        try {
-            writer__.simplex_unconstrain(pi);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable pi: ") + e.what());
-        }
-
-        if (!(context__.contains_r("u")))
-            throw std::runtime_error("variable u missing");
-        vals_r__ = context__.vals_r("u");
-        pos__ = 0U;
-        validate_non_negative_index("u", "K", K);
-        context__.validate_dims("initialization", "u", "vector_d", context__.to_vec(K));
-        // generate_declaration u
-        vector_d u(static_cast<Eigen::VectorXd::Index>(K));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            u(j1__) = vals_r__[pos__++];
-        try {
-            writer__.unit_vector_unconstrain(u);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable u: ") + e.what());
-        }
-
-        if (!(context__.contains_r("R2")))
-            throw std::runtime_error("variable R2 missing");
-        vals_r__ = context__.vals_r("R2");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "R2", "double", context__.to_vec());
-        // generate_declaration R2
-        double R2(0);
-        R2 = vals_r__[pos__++];
-        try {
-            writer__.scalar_lub_unconstrain(0,1,R2);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable R2: ") + e.what());
-        }
-
-        if (!(context__.contains_r("alpha")))
-            throw std::runtime_error("variable alpha missing");
-        vals_r__ = context__.vals_r("alpha");
-        pos__ = 0U;
-        validate_non_negative_index("alpha", "is_skewed", is_skewed);
-        context__.validate_dims("initialization", "alpha", "double", context__.to_vec(is_skewed));
-        // generate_declaration alpha
-        std::vector<double> alpha(is_skewed,double(0));
-        for (int i0__ = 0U; i0__ < is_skewed; ++i0__)
-            alpha[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < is_skewed; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,alpha[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable alpha: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  pi;
-        (void) pi;  // dummy to suppress unused var warning
-        if (jacobian__)
-            pi = in__.simplex_constrain(J,lp__);
-        else
-            pi = in__.simplex_constrain(J);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  u;
-        (void) u;  // dummy to suppress unused var warning
-        if (jacobian__)
-            u = in__.unit_vector_constrain(K,lp__);
-        else
-            u = in__.unit_vector_constrain(K);
-
-        T__ R2;
-        (void) R2;  // dummy to suppress unused var warning
-        if (jacobian__)
-            R2 = in__.scalar_lub_constrain(0,1,lp__);
-        else
-            R2 = in__.scalar_lub_constrain(0,1);
-
-        vector<T__> alpha;
-        size_t dim_alpha_0__ = is_skewed;
-        alpha.reserve(dim_alpha_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_alpha_0__; ++k_0__) {
-            if (jacobian__)
-                alpha.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                alpha.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("cutpoints", "(J - 1)", (J - 1));
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  cutpoints(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) cutpoints;  // dummy to suppress unused var warning
-
-        stan::math::initialize(cutpoints, DUMMY_VAR__);
-        stan::math::fill(cutpoints,DUMMY_VAR__);
-
-
-        try {
-            {
-                T__ Delta_y;
-                (void) Delta_y;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Delta_y, DUMMY_VAR__);
-                stan::math::fill(Delta_y,DUMMY_VAR__);
-                stan::math::assign(Delta_y,inv(sqrt((1 - R2))));
-
-
-                current_statement_begin__ = 224;
-                if (as_bool(logical_gt(K,1))) {
-                    current_statement_begin__ = 225;
-                    stan::math::assign(beta, multiply(multiply(multiply(u,sqrt(R2)),Delta_y),sqrt_Nm1));
-                } else {
-                    current_statement_begin__ = 226;
-                    stan::math::assign(get_base1_lhs(beta,1,"beta",1), (((get_base1(u,1,"u",1) * sqrt(R2)) * Delta_y) * sqrt_Nm1));
-                }
-                current_statement_begin__ = 227;
-                stan::math::assign(cutpoints, make_cutpoints(pi,Delta_y,link, pstream__));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < (J - 1); ++i0__) {
-            if (stan::math::is_uninitialized(cutpoints(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: cutpoints" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 233;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 234;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 234;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 235;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 237;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 238;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 238;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 239;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 239;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 240;
-                if (as_bool((primitive_value(logical_eq(has_weights,0)) && primitive_value(logical_eq(prior_PD,0))))) {
-
-                    current_statement_begin__ = 241;
-                    if (as_bool(logical_eq(is_skewed,0))) {
-                        current_statement_begin__ = 242;
-                        lp_accum__.add(pw_polr(y,eta,cutpoints,link,1.0, pstream__));
-                    } else {
-                        current_statement_begin__ = 243;
-                        lp_accum__.add(pw_polr(y,eta,cutpoints,link,get_base1(alpha,1,"alpha",1), pstream__));
-                    }
-                } else if (as_bool(logical_eq(prior_PD,0))) {
-
-                    current_statement_begin__ = 246;
-                    if (as_bool(logical_eq(is_skewed,0))) {
-                        current_statement_begin__ = 247;
-                        lp_accum__.add(dot_product(weights,pw_polr(y,eta,cutpoints,link,1.0, pstream__)));
-                    } else {
-                        current_statement_begin__ = 248;
-                        lp_accum__.add(dot_product(weights,pw_polr(y,eta,cutpoints,link,get_base1(alpha,1,"alpha",1), pstream__)));
-                    }
-                }
-                current_statement_begin__ = 251;
-                if (as_bool(logical_eq(is_constant,0))) {
-                    current_statement_begin__ = 251;
-                    lp_accum__.add(dirichlet_log(pi,prior_counts));
-                }
-                current_statement_begin__ = 253;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 253;
-                    lp_accum__.add(beta_log(R2,half_K,regularization));
-                }
-                current_statement_begin__ = 254;
-                if (as_bool(logical_eq(is_skewed,1))) {
-                    current_statement_begin__ = 254;
-                    lp_accum__.add(gamma_log(alpha,shape,rate));
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("pi");
-        names__.push_back("u");
-        names__.push_back("R2");
-        names__.push_back("alpha");
-        names__.push_back("beta");
-        names__.push_back("cutpoints");
-        names__.push_back("mean_PPD");
-        names__.push_back("residuals");
-        names__.push_back("zeta");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(J);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(is_skewed);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J - 1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_gt(J,2) ? J : 1 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((do_residuals ? N : 0 ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((J - 1));
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_polr_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector_d pi = in__.simplex_constrain(J);
-        vector_d u = in__.unit_vector_constrain(K);
-        double R2 = in__.scalar_lub_constrain(0,1);
-        vector<double> alpha;
-        size_t dim_alpha_0__ = is_skewed;
-        for (size_t k_0__ = 0; k_0__ < dim_alpha_0__; ++k_0__) {
-            alpha.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < J; ++k_0__) {
-            vars__.push_back(pi[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(u[k_0__]);
-        }
-        vars__.push_back(R2);
-        for (int k_0__ = 0; k_0__ < is_skewed; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        validate_non_negative_index("cutpoints", "(J - 1)", (J - 1));
-        vector_d cutpoints(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) cutpoints;  // dummy to suppress unused var warning
-
-        stan::math::initialize(cutpoints, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(cutpoints,DUMMY_VAR__);
-
-
-        try {
-            {
-                double Delta_y(0.0);
-                (void) Delta_y;  // dummy to suppress unused var warning
-
-                stan::math::initialize(Delta_y, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(Delta_y,DUMMY_VAR__);
-                stan::math::assign(Delta_y,inv(sqrt((1 - R2))));
-
-
-                current_statement_begin__ = 224;
-                if (as_bool(logical_gt(K,1))) {
-                    current_statement_begin__ = 225;
-                    stan::math::assign(beta, multiply(multiply(multiply(u,sqrt(R2)),Delta_y),sqrt_Nm1));
-                } else {
-                    current_statement_begin__ = 226;
-                    stan::math::assign(get_base1_lhs(beta,1,"beta",1), (((get_base1(u,1,"u",1) * sqrt(R2)) * Delta_y) * sqrt_Nm1));
-                }
-                current_statement_begin__ = 227;
-                stan::math::assign(cutpoints, make_cutpoints(pi,Delta_y,link, pstream__));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J - 1); ++k_0__) {
-            vars__.push_back(cutpoints[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        validate_non_negative_index("mean_PPD", "(logical_gt(J,2) ? J : 1 )", (logical_gt(J,2) ? J : 1 ));
-        vector_d mean_PPD(static_cast<Eigen::VectorXd::Index>((logical_gt(J,2) ? J : 1 )));
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,rep_vector(0,(logical_gt(J,2) ? J : 1 )));
-        validate_non_negative_index("residuals", "(do_residuals ? N : 0 )", (do_residuals ? N : 0 ));
-        vector_d residuals(static_cast<Eigen::VectorXd::Index>((do_residuals ? N : 0 )));
-        (void) residuals;  // dummy to suppress unused var warning
-
-        stan::math::initialize(residuals, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(residuals,DUMMY_VAR__);
-        validate_non_negative_index("zeta", "(J - 1)", (J - 1));
-        vector_d zeta(static_cast<Eigen::VectorXd::Index>((J - 1)));
-        (void) zeta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(zeta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(zeta,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 262;
-            if (as_bool(dense_X)) {
-                current_statement_begin__ = 262;
-                stan::math::assign(zeta, add(cutpoints,dot_product(xbar,beta)));
-            } else {
-                current_statement_begin__ = 263;
-                stan::math::assign(zeta, cutpoints);
-            }
-            current_statement_begin__ = 264;
-            if (as_bool(logical_eq(J,2))) {
-                current_statement_begin__ = 264;
-                stan::math::assign(zeta, minus(zeta));
-            }
-            {
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 268;
-                if (as_bool(logical_gt(K,0))) {
-
-                    current_statement_begin__ = 269;
-                    if (as_bool(dense_X)) {
-                        current_statement_begin__ = 269;
-                        stan::math::assign(eta, multiply(get_base1(X,1,"X",1),beta));
-                    } else {
-                        current_statement_begin__ = 270;
-                        stan::math::assign(eta, csr_matrix_times_vector(N,K,w_X,v_X,u_X,beta));
-                    }
-                } else {
-                    current_statement_begin__ = 272;
-                    stan::math::assign(eta, rep_vector(0.0,N));
-                }
-                current_statement_begin__ = 273;
-                if (as_bool(logical_eq(has_offset,1))) {
-                    current_statement_begin__ = 273;
-                    stan::math::assign(eta, add(eta,offset));
-                }
-                current_statement_begin__ = 274;
-                if (as_bool(K_smooth)) {
-                    current_statement_begin__ = 274;
-                    stan::math::assign(eta, add(eta,multiply(S,beta_smooth)));
-                }
-                current_statement_begin__ = 275;
-                for (int n = 1; n <= N; ++n) {
-                    {
-                        int y_tilde(0);
-                        (void) y_tilde;  // dummy to suppress unused var warning
-
-                        stan::math::fill(y_tilde, std::numeric_limits<int>::min());
-                        validate_non_negative_index("theta", "J", J);
-                        vector_d theta(static_cast<Eigen::VectorXd::Index>(J));
-                        (void) theta;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(theta, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(theta,DUMMY_VAR__);
-                        double previous(0.0);
-                        (void) previous;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(previous, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(previous,DUMMY_VAR__);
-
-
-                        current_statement_begin__ = 279;
-                        stan::math::assign(get_base1_lhs(theta,1,"theta",1), CDF_polr((get_base1(cutpoints,1,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                        current_statement_begin__ = 280;
-                        stan::math::assign(previous, get_base1(theta,1,"theta",1));
-                        current_statement_begin__ = 281;
-                        if (as_bool(is_skewed)) {
-                            current_statement_begin__ = 281;
-                            stan::math::assign(get_base1_lhs(theta,1,"theta",1), pow(get_base1(theta,1,"theta",1),get_base1(alpha,1,"alpha",1)));
-                        }
-                        current_statement_begin__ = 282;
-                        for (int j = 2; j <= (J - 1); ++j) {
-                            {
-                                double current(0.0);
-                                (void) current;  // dummy to suppress unused var warning
-
-                                stan::math::initialize(current, std::numeric_limits<double>::quiet_NaN());
-                                stan::math::fill(current,DUMMY_VAR__);
-
-
-                                current_statement_begin__ = 284;
-                                stan::math::assign(current, CDF_polr((get_base1(cutpoints,j,"cutpoints",1) - get_base1(eta,n,"eta",1)),link, pstream__));
-                                current_statement_begin__ = 285;
-                                stan::math::assign(get_base1_lhs(theta,j,"theta",1), (current - previous));
-                                current_statement_begin__ = 286;
-                                stan::math::assign(previous, current);
-                            }
-                        }
-                        current_statement_begin__ = 288;
-                        if (as_bool(logical_eq(is_skewed,0))) {
-                            current_statement_begin__ = 288;
-                            stan::math::assign(get_base1_lhs(theta,J,"theta",1), (1 - previous));
-                        } else {
-                            current_statement_begin__ = 289;
-                            stan::math::assign(get_base1_lhs(theta,J,"theta",1), (1 - pow(previous,get_base1(alpha,1,"alpha",1))));
-                        }
-                        current_statement_begin__ = 290;
-                        if (as_bool((primitive_value(logical_lte(previous,0)) || primitive_value(logical_gte(previous,1))))) {
-
-                        } else if (as_bool(logical_eq(J,2))) {
-
-                            current_statement_begin__ = 294;
-                            stan::math::assign(get_base1_lhs(mean_PPD,1,"mean_PPD",1), (get_base1(mean_PPD,1,"mean_PPD",1) + bernoulli_rng(get_base1(theta,J,"theta",1), base_rng__)));
-                        } else {
-
-                            current_statement_begin__ = 297;
-                            stan::math::assign(y_tilde, categorical_rng(theta, base_rng__));
-                            current_statement_begin__ = 298;
-                            stan::math::assign(get_base1_lhs(mean_PPD,y_tilde,"mean_PPD",1), (get_base1(mean_PPD,y_tilde,"mean_PPD",1) + 1));
-                        }
-                        current_statement_begin__ = 301;
-                        if (as_bool(do_residuals)) {
-                            {
-                                double ystar(0.0);
-                                (void) ystar;  // dummy to suppress unused var warning
-
-                                stan::math::initialize(ystar, std::numeric_limits<double>::quiet_NaN());
-                                stan::math::fill(ystar,DUMMY_VAR__);
-
-
-                                current_statement_begin__ = 303;
-                                if (as_bool(logical_eq(get_base1(y,n,"y",1),1))) {
-                                    current_statement_begin__ = 304;
-                                    stan::math::assign(ystar, draw_ystar_rng(stan::math::negative_infinity(),get_base1(cutpoints,1,"cutpoints",1),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                } else if (as_bool(logical_eq(get_base1(y,n,"y",1),J))) {
-                                    current_statement_begin__ = 306;
-                                    stan::math::assign(ystar, draw_ystar_rng(get_base1(cutpoints,(J - 1),"cutpoints",1),stan::math::positive_infinity(),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                } else {
-                                    current_statement_begin__ = 307;
-                                    stan::math::assign(ystar, draw_ystar_rng(get_base1(cutpoints,(get_base1(y,n,"y",1) - 1),"cutpoints",1),get_base1(cutpoints,get_base1(y,n,"y",1),"cutpoints",1),get_base1(eta,n,"eta",1),link, base_rng__, pstream__));
-                                }
-                                current_statement_begin__ = 308;
-                                stan::math::assign(get_base1_lhs(residuals,n,"residuals",1), (ystar - get_base1(eta,n,"eta",1)));
-                            }
-                        }
-                    }
-                }
-                current_statement_begin__ = 311;
-                stan::math::assign(mean_PPD, divide(mean_PPD,N));
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        for (int k_0__ = 0; k_0__ < (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            vars__.push_back(mean_PPD[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (do_residuals ? N : 0 ); ++k_0__) {
-            vars__.push_back(residuals[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (J - 1); ++k_0__) {
-            vars__.push_back(zeta[k_0__]);
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_polr";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= J; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "pi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "u" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "R2";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= is_skewed; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "cutpoints" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (do_residuals ? N : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "residuals" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "pi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "u" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "R2";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= is_skewed; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "cutpoints" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        for (int k_0__ = 1; k_0__ <= (logical_gt(J,2) ? J : 1 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "mean_PPD" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (do_residuals ? N : 0 ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "residuals" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (J - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "zeta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-// Code generated by Stan version 2.16.0
-
-#include <stan/model/model_header.hpp>
-
-namespace model_spatial_namespace {
-
-using std::istream;
-using std::string;
-using std::stringstream;
-using std::vector;
-using stan::io::dump;
-using stan::math::lgamma;
-using stan::model::prob_grad;
-using namespace stan::math;
-
-typedef Eigen::Matrix<double,Eigen::Dynamic,1> vector_d;
-typedef Eigen::Matrix<double,1,Eigen::Dynamic> row_vector_d;
-typedef Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> matrix_d;
-
-static int current_statement_begin__;
-
-stan::io::program_reader prog_reader__() {
-    stan::io::program_reader reader;
-    reader.add_event(0, 0, "start", "model_spatial");
-    reader.add_event(968, 968, "end", "model_spatial");
-    return reader;
-}
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 31;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 31;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 32;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 33;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            current_statement_begin__ = 34;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 34;
-        current_statement_begin__ = 35;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gauss_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gauss(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gauss(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 46;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(subtract((-(0.5) * log((6.2831853071795862 * sigma))),multiply(0.5,square(divide(subtract(y,linkinv_gauss(eta,link, pstream__)),sigma)))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gauss_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& sigma,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gauss(y, eta, sigma, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 58;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 58;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 59;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 60;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else {
-            current_statement_begin__ = 61;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 61;
-        current_statement_begin__ = 62;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_gamma_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_gamma(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-GammaReg(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ ret;
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-            stan::math::assign(ret,((rows(y) * ((shape * log(shape)) - stan::math::lgamma(shape))) + ((shape - 1) * sum_log_y)));
-
-
-            current_statement_begin__ = 79;
-            if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 80;
-                stan::math::assign(ret, ((ret - (shape * sum(eta))) - (shape * sum(elt_divide(y,exp(eta))))));
-            } else if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 82;
-                stan::math::assign(ret, ((ret - (shape * sum(log(eta)))) - (shape * sum(elt_divide(y,eta)))));
-            } else if (as_bool(logical_eq(link,3))) {
-                current_statement_begin__ = 84;
-                stan::math::assign(ret, ((ret + (shape * sum(log(eta)))) - (shape * dot_product(eta,y))));
-            } else {
-                current_statement_begin__ = 85;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 85;
-            current_statement_begin__ = 86;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct GammaReg_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link,
-             const T4__& sum_log_y, std::ostream* pstream__) const {
-        return GammaReg(y, eta, shape, link, sum_log_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_gamma(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 100;
-            if (as_bool(logical_eq(link,3))) {
-
-                current_statement_begin__ = 101;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 102;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape * get_base1(eta,n,"eta",1))));
-                }
-            } else if (as_bool(logical_eq(link,2))) {
-
-                current_statement_begin__ = 106;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 107;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / exp(get_base1(eta,n,"eta",1)))));
-                }
-            } else if (as_bool(logical_eq(link,1))) {
-
-                current_statement_begin__ = 111;
-                for (int n = 1; n <= N; ++n) {
-
-                    current_statement_begin__ = 112;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), gamma_log(get_base1(y,n,"y",1),shape,(shape / get_base1(eta,n,"eta",1))));
-                }
-            } else {
-                current_statement_begin__ = 115;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 115;
-            current_statement_begin__ = 116;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_gamma_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-             const T2__& shape,
-             const int& link, std::ostream* pstream__) const {
-        return pw_gamma(y, eta, shape, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 127;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 127;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 128;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 129;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 130;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_sqrt(eta));
-        } else {
-            current_statement_begin__ = 131;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 131;
-        current_statement_begin__ = 132;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_inv_gaussian_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                         const int& link, std::ostream* pstream__) const {
-        return linkinv_inv_gaussian(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 147;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(((((0.5 * rows(y)) * log((lambda / 6.2831853071795862))) - (1.5 * sum_log_y)) - ((0.5 * lambda) * dot_self(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y))))));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                 const Eigen::Matrix<T1__, Eigen::Dynamic,1>& mu,
-                 const T2__& lambda,
-                 const T3__& sum_log_y,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return inv_gaussian(y, mu, lambda, sum_log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-pw_inv_gaussian(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_inv_gaussian(eta,link, pstream__));
-
-
-            current_statement_begin__ = 166;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(add(multiply((-(0.5) * lambda),square(elt_divide(subtract(y,mu),elt_multiply(mu,sqrt_y)))),(0.5 * log((lambda / 6.2831853071795862)))),multiply(1.5,log_y)));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_inv_gaussian_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T4__, typename T5__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T4__, typename boost::math::tools::promote_args<T5__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-                    const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-                    const T2__& lambda,
-                    const int& link,
-                    const Eigen::Matrix<T4__, Eigen::Dynamic,1>& log_y,
-                    const Eigen::Matrix<T5__, Eigen::Dynamic,1>& sqrt_y, std::ostream* pstream__) const {
-        return pw_inv_gaussian(y, eta, lambda, link, log_y, sqrt_y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, class RNG>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-inv_gaussian_rng(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ mu2;
-            (void) mu2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu2,DUMMY_VAR__);
-            stan::math::assign(mu2,square(mu));
-            fun_scalar_t__ z;
-            (void) z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z,DUMMY_VAR__);
-            stan::math::assign(z,uniform_rng(0,1, base_rng__));
-            fun_scalar_t__ y;
-            (void) y;  // dummy to suppress unused var warning
-
-            stan::math::initialize(y, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(y,DUMMY_VAR__);
-            stan::math::assign(y,square(normal_rng(0,1, base_rng__)));
-            fun_scalar_t__ x;
-            (void) x;  // dummy to suppress unused var warning
-
-            stan::math::initialize(x, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(x,DUMMY_VAR__);
-            stan::math::assign(x,(mu + (((mu2 * y) - (mu * sqrt(((((4 * mu) * lambda) * y) + (mu2 * square(y)))))) / (2 * lambda))));
-
-
-            current_statement_begin__ = 185;
-            if (as_bool(logical_lte(z,(mu / (mu + x))))) {
-                current_statement_begin__ = 185;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(x);
-            } else {
-                current_statement_begin__ = 186;
-                return stan::math::promote_scalar<fun_return_scalar_t__>((mu2 / x));
-            }
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct inv_gaussian_rng_functor__ {
-    template <typename T0__, typename T1__, class RNG>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& mu,
-                     const T1__& lambda, RNG& base_rng__, std::ostream* pstream__) const {
-        return inv_gaussian_rng(mu, lambda, base_rng__, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 197;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 197;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 198;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 199;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 200;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(0.5,divide(atan(eta),stan::math::pi())));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 201;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,6))) {
-            current_statement_begin__ = 202;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(subtract(1,inv_cloglog(minus(eta))));
-        } else {
-            current_statement_begin__ = 203;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 203;
-        current_statement_begin__ = 204;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                 const int& link, std::ostream* pstream__) const {
-        return linkinv_beta(eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 215;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 215;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 216;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 217;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            current_statement_begin__ = 218;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 219;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_beta_z_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                   const int& link, std::ostream* pstream__) const {
-        return linkinv_beta_z(eta, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-
-
-            current_statement_begin__ = 234;
-            for (int n = 1; n <= rows(y); ++n) {
-
-                current_statement_begin__ = 235;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * dispersion),((1 - get_base1(mu,n,"mu",1)) * dispersion)));
-            }
-            current_statement_begin__ = 237;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const T2__& dispersion,
-            const int& link, std::ostream* pstream__) const {
-        return pw_beta(y, eta, dispersion, link, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-pw_beta_z(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("ll", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-            validate_non_negative_index("mu", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu,DUMMY_VAR__);
-            stan::math::assign(mu,linkinv_beta(eta,link, pstream__));
-            validate_non_negative_index("mu_z", "rows(y)", rows(y));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  mu_z(static_cast<Eigen::VectorXd::Index>(rows(y)));
-            (void) mu_z;  // dummy to suppress unused var warning
-
-            stan::math::initialize(mu_z, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(mu_z,DUMMY_VAR__);
-            stan::math::assign(mu_z,linkinv_beta_z(eta_z,link_phi, pstream__));
-
-
-            current_statement_begin__ = 254;
-            for (int n = 1; n <= rows(y); ++n) {
-
-                current_statement_begin__ = 255;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), beta_log(get_base1(y,n,"y",1),(get_base1(mu,n,"mu",1) * get_base1(mu_z,n,"mu_z",1)),((1 - get_base1(mu,n,"mu",1)) * get_base1(mu_z,n,"mu_z",1))));
-            }
-            current_statement_begin__ = 257;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_beta_z_functor__ {
-    template <typename T0__, typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& y,
-              const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-              const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta_z,
-              const int& link,
-              const int& link_phi, std::ostream* pstream__) const {
-        return pw_beta_z(y, eta, eta_z, link, link_phi, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_binom(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 268;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 268;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_logit(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 269;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(Phi(eta));
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 270;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(add(divide(atan(eta),stan::math::pi()),0.5));
-        } else if (as_bool(logical_eq(link,4))) {
-            current_statement_begin__ = 271;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,5))) {
-            current_statement_begin__ = 272;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(inv_cloglog(eta));
-        } else {
-            current_statement_begin__ = 273;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 273;
-        current_statement_begin__ = 274;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_binom_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_binom(eta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T_lp__, typename T_lp_accum__>
-typename boost::math::tools::promote_args<T2__, T_lp__>::type
-ll_binom_lp(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T_lp__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 286;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 286;
-            lp_accum__.add(binomial_logit_log(y,trials,eta));
-        } else if (as_bool(logical_lt(link,4))) {
-            current_statement_begin__ = 287;
-            lp_accum__.add(binomial_log(y,trials,linkinv_binom(eta,link, pstream__)));
-        } else if (as_bool(logical_eq(link,4))) {
-
-            current_statement_begin__ = 289;
-            for (int n = 1; n <= num_elements(y); ++n) {
-
-                current_statement_begin__ = 290;
-                lp_accum__.add((get_base1(y,n,"y",1) * get_base1(eta,n,"eta",1)));
-                current_statement_begin__ = 291;
-                lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * log1m_exp(get_base1(eta,n,"eta",1))));
-                current_statement_begin__ = 292;
-                lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-            }
-        } else if (as_bool(logical_eq(link,5))) {
-
-            current_statement_begin__ = 296;
-            for (int n = 1; n <= num_elements(y); ++n) {
-                {
-                    fun_scalar_t__ neg_exp_eta;
-                    (void) neg_exp_eta;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(neg_exp_eta, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(neg_exp_eta,DUMMY_VAR__);
-                    stan::math::assign(neg_exp_eta,-(exp(get_base1(eta,n,"eta",1))));
-
-
-                    current_statement_begin__ = 298;
-                    lp_accum__.add((get_base1(y,n,"y",1) * log1m_exp(neg_exp_eta)));
-                    current_statement_begin__ = 299;
-                    lp_accum__.add(((get_base1(trials,n,"trials",1) - get_base1(y,n,"y",1)) * neg_exp_eta));
-                    current_statement_begin__ = 300;
-                    lp_accum__.add(binomial_coefficient_log(get_base1(trials,n,"trials",1),get_base1(y,n,"y",1)));
-                }
-            }
-        } else {
-            current_statement_begin__ = 303;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 303;
-        current_statement_begin__ = 304;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(get_lp(lp__, lp_accum__));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct ll_binom_lp_functor__ {
-    template <typename T2__, typename T_lp__, typename T_lp_accum__>
-        typename boost::math::tools::promote_args<T2__, T_lp__>::type
-    operator()(const std::vector<int>& y,
-                const std::vector<int>& trials,
-                const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-                const int& link, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return ll_binom_lp(y, trials, eta, link, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-pw_binom(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 317;
-            if (as_bool(logical_eq(link,1))) {
-
-                current_statement_begin__ = 318;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 319;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_logit_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,5))) {
-                {
-                    validate_non_negative_index("pi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) pi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(pi,DUMMY_VAR__);
-                    stan::math::assign(pi,linkinv_binom(eta,link, pstream__));
-
-
-                    current_statement_begin__ = 323;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 323;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), binomial_log(get_base1(y,n,"y",1),get_base1(trials,n,"trials",1),get_base1(pi,n,"pi",1)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 325;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 325;
-            current_statement_begin__ = 326;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_binom_functor__ {
-    template <typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-             const std::vector<int>& trials,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& eta,
-             const int& link, std::ostream* pstream__) const {
-        return pw_binom(y, trials, eta, link, pstream__);
-    }
-};
-
-template <typename T0__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-linkinv_count(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 338;
-        if (as_bool(logical_eq(link,1))) {
-            current_statement_begin__ = 338;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(exp(eta));
-        } else if (as_bool(logical_eq(link,2))) {
-            current_statement_begin__ = 339;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-        } else if (as_bool(logical_eq(link,3))) {
-            current_statement_begin__ = 340;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(square(eta));
-        } else {
-            current_statement_begin__ = 341;
-            std::stringstream errmsg_stream__;
-            errmsg_stream__ << "Invalid link";
-            throw std::domain_error(errmsg_stream__.str());
-        }
-        current_statement_begin__ = 341;
-        current_statement_begin__ = 342;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(eta);
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct linkinv_count_functor__ {
-    template <typename T0__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& eta,
-                  const int& link, std::ostream* pstream__) const {
-        return linkinv_count(eta, link, pstream__);
-    }
-};
-
-template <typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-pw_pois(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 356;
-            if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 357;
-                for (int n = 1; n <= N; ++n) {
-                    current_statement_begin__ = 357;
-                    stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log_log(get_base1(y,n,"y",1),get_base1(eta,n,"eta",1)));
-                }
-            } else if (as_bool(logical_lte(link,3))) {
-                {
-                    validate_non_negative_index("phi", "N", N);
-                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-                    (void) phi;  // dummy to suppress unused var warning
-
-                    stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-                    stan::math::fill(phi,DUMMY_VAR__);
-                    stan::math::assign(phi,linkinv_count(eta,link, pstream__));
-
-
-                    current_statement_begin__ = 360;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 360;
-                        stan::math::assign(get_base1_lhs(ll,n,"ll",1), poisson_log(get_base1(y,n,"y",1),get_base1(phi,n,"phi",1)));
-                    }
-                }
-            } else {
-                current_statement_begin__ = 362;
-                std::stringstream errmsg_stream__;
-                errmsg_stream__ << "Invalid link";
-                throw std::domain_error(errmsg_stream__.str());
-            }
-            current_statement_begin__ = 362;
-            current_statement_begin__ = 363;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_pois_functor__ {
-    template <typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-            const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-            const int& link, std::ostream* pstream__) const {
-        return pw_pois(y, eta, link, pstream__);
-    }
-};
-
-template <typename T1__, typename T2__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-pw_nb(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T1__, T2__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int N(0);
-            (void) N;  // dummy to suppress unused var warning
-
-            stan::math::fill(N, std::numeric_limits<int>::min());
-            stan::math::assign(N,rows(eta));
-            validate_non_negative_index("rho", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  rho(static_cast<Eigen::VectorXd::Index>(N));
-            (void) rho;  // dummy to suppress unused var warning
-
-            stan::math::initialize(rho, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(rho,DUMMY_VAR__);
-            stan::math::assign(rho,linkinv_count(eta,link, pstream__));
-            validate_non_negative_index("ll", "N", N);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ll(static_cast<Eigen::VectorXd::Index>(N));
-            (void) ll;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ll, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ll,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 379;
-            for (int n = 1; n <= N; ++n) {
-                current_statement_begin__ = 379;
-                stan::math::assign(get_base1_lhs(ll,n,"ll",1), neg_binomial_2_log(get_base1(y,n,"y",1),get_base1(rho,n,"rho",1),theta));
-            }
-            current_statement_begin__ = 380;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ll);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct pw_nb_functor__ {
-    template <typename T1__, typename T2__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T1__, T2__>::type, Eigen::Dynamic,1>
-    operator()(const std::vector<int>& y,
-          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& eta,
-          const T2__& theta,
-          const int& link, std::ostream* pstream__) const {
-        return pw_nb(y, eta, theta, link, pstream__);
-    }
-};
-
-template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-make_theta_L(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("theta_L", "len_theta_L", len_theta_L);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  theta_L(static_cast<Eigen::VectorXd::Index>(len_theta_L));
-            (void) theta_L;  // dummy to suppress unused var warning
-
-            stan::math::initialize(theta_L, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(theta_L,DUMMY_VAR__);
-            int zeta_mark(0);
-            (void) zeta_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(zeta_mark, std::numeric_limits<int>::min());
-            stan::math::assign(zeta_mark,1);
-            int rho_mark(0);
-            (void) rho_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(rho_mark, std::numeric_limits<int>::min());
-            stan::math::assign(rho_mark,1);
-            int z_T_mark(0);
-            (void) z_T_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(z_T_mark, std::numeric_limits<int>::min());
-            stan::math::assign(z_T_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 411;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 413;
-                    if (as_bool(logical_eq(nc,1))) {
-
-                        current_statement_begin__ = 414;
-                        stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), ((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion));
-                        current_statement_begin__ = 416;
-                        stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            fun_scalar_t__ std_dev;
-                            (void) std_dev;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(std_dev, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(std_dev,DUMMY_VAR__);
-                            fun_scalar_t__ T21;
-                            (void) T21;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T21, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T21,DUMMY_VAR__);
-                            fun_scalar_t__ trace_T_i;
-                            (void) trace_T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(trace_T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(trace_T_i,DUMMY_VAR__);
-                            stan::math::assign(trace_T_i,(square(((get_base1(tau,i,"tau",1) * get_base1(scale,i,"scale",1)) * dispersion)) * nc));
-                            validate_non_negative_index("pi", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  pi(static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) pi;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(pi, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(pi,DUMMY_VAR__);
-                            stan::math::assign(pi,segment(zeta,zeta_mark,nc));
-
-
-                            current_statement_begin__ = 424;
-                            stan::math::assign(pi, divide(pi,sum(pi)));
-                            current_statement_begin__ = 427;
-                            stan::math::assign(zeta_mark, (zeta_mark + nc));
-                            current_statement_begin__ = 428;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,1,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 429;
-                            stan::math::assign(get_base1_lhs(T_i,1,1,"T_i",1), std_dev);
-                            current_statement_begin__ = 432;
-                            stan::math::assign(std_dev, sqrt((get_base1(pi,2,"pi",1) * trace_T_i)));
-                            current_statement_begin__ = 433;
-                            stan::math::assign(T21, ((2.0 * get_base1(rho,rho_mark,"rho",1)) - 1.0));
-                            current_statement_begin__ = 434;
-                            stan::math::assign(rho_mark, (rho_mark + 1));
-                            current_statement_begin__ = 435;
-                            stan::math::assign(get_base1_lhs(T_i,2,2,"T_i",1), (std_dev * sqrt((1.0 - square(T21)))));
-                            current_statement_begin__ = 436;
-                            stan::math::assign(get_base1_lhs(T_i,2,1,"T_i",1), (std_dev * T21));
-                            current_statement_begin__ = 438;
-                            for (int r = 2; r <= (nc - 1); ++r) {
-                                {
-                                    int rp1(0);
-                                    (void) rp1;  // dummy to suppress unused var warning
-
-                                    stan::math::fill(rp1, std::numeric_limits<int>::min());
-                                    stan::math::assign(rp1,(r + 1));
-                                    validate_non_negative_index("T_row", "r", r);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  T_row(static_cast<Eigen::VectorXd::Index>(r));
-                                    (void) T_row;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(T_row, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(T_row,DUMMY_VAR__);
-                                    stan::math::assign(T_row,segment(z_T,z_T_mark,r));
-                                    fun_scalar_t__ scale_factor;
-                                    (void) scale_factor;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(scale_factor, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(scale_factor,DUMMY_VAR__);
-                                    stan::math::assign(scale_factor,(sqrt((get_base1(rho,rho_mark,"rho",1) / dot_self(T_row))) * std_dev));
-
-
-                                    current_statement_begin__ = 442;
-                                    stan::math::assign(z_T_mark, (z_T_mark + r));
-                                    current_statement_begin__ = 443;
-                                    stan::math::assign(std_dev, sqrt((get_base1(pi,rp1,"pi",1) * trace_T_i)));
-                                    current_statement_begin__ = 444;
-                                    for (int c = 1; c <= r; ++c) {
-                                        current_statement_begin__ = 444;
-                                        stan::math::assign(get_base1_lhs(T_i,rp1,c,"T_i",1), (get_base1(T_row,c,"T_row",1) * scale_factor));
-                                    }
-                                    current_statement_begin__ = 445;
-                                    stan::math::assign(get_base1_lhs(T_i,rp1,rp1,"T_i",1), (sqrt((1.0 - get_base1(rho,rho_mark,"rho",1))) * std_dev));
-                                    current_statement_begin__ = 446;
-                                    stan::math::assign(rho_mark, (rho_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 450;
-                            for (int c = 1; c <= nc; ++c) {
-                                current_statement_begin__ = 450;
-                                for (int r = c; r <= nc; ++r) {
-
-                                    current_statement_begin__ = 451;
-                                    stan::math::assign(get_base1_lhs(theta_L,theta_L_mark,"theta_L",1), get_base1(T_i,r,c,"T_i",1));
-                                    current_statement_begin__ = 452;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 456;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(theta_L);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_theta_L_functor__ {
-    template <typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T2__, T3__, T4__, T5__, typename boost::math::tools::promote_args<T6__, T7__>::type>::type, Eigen::Dynamic,1>
-    operator()(const int& len_theta_L,
-                 const std::vector<int>& p,
-                 const T2__& dispersion,
-                 const Eigen::Matrix<T3__, Eigen::Dynamic,1>& tau,
-                 const Eigen::Matrix<T4__, Eigen::Dynamic,1>& scale,
-                 const Eigen::Matrix<T5__, Eigen::Dynamic,1>& zeta,
-                 const Eigen::Matrix<T6__, Eigen::Dynamic,1>& rho,
-                 const Eigen::Matrix<T7__, Eigen::Dynamic,1>& z_T, std::ostream* pstream__) const {
-        return make_theta_L(len_theta_L, p, dispersion, tau, scale, zeta, rho, z_T, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-make_b(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("b", "rows(z_b)", rows(z_b));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  b(static_cast<Eigen::VectorXd::Index>(rows(z_b)));
-            (void) b;  // dummy to suppress unused var warning
-
-            stan::math::initialize(b, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(b,DUMMY_VAR__);
-            int b_mark(0);
-            (void) b_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(b_mark, std::numeric_limits<int>::min());
-            stan::math::assign(b_mark,1);
-            int theta_L_mark(0);
-            (void) theta_L_mark;  // dummy to suppress unused var warning
-
-            stan::math::fill(theta_L_mark, std::numeric_limits<int>::min());
-            stan::math::assign(theta_L_mark,1);
-
-
-            current_statement_begin__ = 474;
-            for (int i = 1; i <= size(p); ++i) {
-                {
-                    int nc(0);
-                    (void) nc;  // dummy to suppress unused var warning
-
-                    stan::math::fill(nc, std::numeric_limits<int>::min());
-                    stan::math::assign(nc,get_base1(p,i,"p",1));
-
-
-                    current_statement_begin__ = 476;
-                    if (as_bool(logical_eq(nc,1))) {
-                        {
-                            fun_scalar_t__ theta_L_start;
-                            (void) theta_L_start;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(theta_L_start, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(theta_L_start,DUMMY_VAR__);
-                            stan::math::assign(theta_L_start,get_base1(theta_L,theta_L_mark,"theta_L",1));
-
-
-                            current_statement_begin__ = 478;
-                            for (int s = b_mark; s <= ((b_mark + get_base1(l,i,"l",1)) - 1); ++s) {
-                                current_statement_begin__ = 479;
-                                stan::math::assign(get_base1_lhs(b,s,"b",1), (theta_L_start * get_base1(z_b,s,"z_b",1)));
-                            }
-                            current_statement_begin__ = 480;
-                            stan::math::assign(b_mark, (b_mark + get_base1(l,i,"l",1)));
-                            current_statement_begin__ = 481;
-                            stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                        }
-                    } else {
-                        {
-                            validate_non_negative_index("T_i", "nc", nc);
-                            validate_non_negative_index("T_i", "nc", nc);
-                            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,Eigen::Dynamic>  T_i(static_cast<Eigen::VectorXd::Index>(nc),static_cast<Eigen::VectorXd::Index>(nc));
-                            (void) T_i;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(T_i, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(T_i,DUMMY_VAR__);
-                            stan::math::assign(T_i,rep_matrix(0,nc,nc));
-
-
-                            current_statement_begin__ = 485;
-                            for (int c = 1; c <= nc; ++c) {
-
-                                current_statement_begin__ = 486;
-                                stan::math::assign(get_base1_lhs(T_i,c,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                current_statement_begin__ = 487;
-                                stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                current_statement_begin__ = 488;
-                                for (int r = (c + 1); r <= nc; ++r) {
-
-                                    current_statement_begin__ = 489;
-                                    stan::math::assign(get_base1_lhs(T_i,r,c,"T_i",1), get_base1(theta_L,theta_L_mark,"theta_L",1));
-                                    current_statement_begin__ = 490;
-                                    stan::math::assign(theta_L_mark, (theta_L_mark + 1));
-                                }
-                            }
-                            current_statement_begin__ = 493;
-                            for (int j = 1; j <= get_base1(l,i,"l",1); ++j) {
-                                {
-                                    validate_non_negative_index("temp", "nc", nc);
-                                    Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  temp(static_cast<Eigen::VectorXd::Index>(nc));
-                                    (void) temp;  // dummy to suppress unused var warning
-
-                                    stan::math::initialize(temp, std::numeric_limits<double>::quiet_NaN());
-                                    stan::math::fill(temp,DUMMY_VAR__);
-                                    stan::math::assign(temp,multiply(T_i,segment(z_b,b_mark,nc)));
-
-
-                                    current_statement_begin__ = 495;
-                                    stan::math::assign(b_mark, (b_mark - 1));
-                                    current_statement_begin__ = 496;
-                                    for (int s = 1; s <= nc; ++s) {
-                                        current_statement_begin__ = 496;
-                                        stan::math::assign(get_base1_lhs(b,(b_mark + s),"b",1), get_base1(temp,s,"temp",1));
-                                    }
-                                    current_statement_begin__ = 497;
-                                    stan::math::assign(b_mark, ((b_mark + nc) + 1));
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-            current_statement_begin__ = 501;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(b);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_b_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-           const Eigen::Matrix<T1__, Eigen::Dynamic,1>& theta_L,
-           const std::vector<int>& p,
-           const std::vector<int>& l, std::ostream* pstream__) const {
-        return make_b(z_b, theta_L, p, l, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-void
-decov_lp(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__, T5__, T6__, T7__, T_lp__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int pos_reg(0);
-            (void) pos_reg;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_reg, std::numeric_limits<int>::min());
-            stan::math::assign(pos_reg,1);
-            int pos_rho(0);
-            (void) pos_rho;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos_rho, std::numeric_limits<int>::min());
-            stan::math::assign(pos_rho,1);
-
-
-            current_statement_begin__ = 524;
-            lp_accum__.add(normal_log(z_b,0,1));
-            current_statement_begin__ = 525;
-            lp_accum__.add(normal_log(z_T,0,1));
-            current_statement_begin__ = 526;
-            for (int i = 1; i <= t; ++i) {
-                current_statement_begin__ = 526;
-                if (as_bool(logical_gt(get_base1(p,i,"p",1),1))) {
-                    {
-                        validate_non_negative_index("shape1", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape1(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape1;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape1, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape1,DUMMY_VAR__);
-                        validate_non_negative_index("shape2", "(get_base1(p,i,\"p\",1) - 1)", (get_base1(p,i,"p",1) - 1));
-                        Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  shape2(static_cast<Eigen::VectorXd::Index>((get_base1(p,i,"p",1) - 1)));
-                        (void) shape2;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(shape2, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(shape2,DUMMY_VAR__);
-                        fun_scalar_t__ nu;
-                        (void) nu;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(nu, std::numeric_limits<double>::quiet_NaN());
-                        stan::math::fill(nu,DUMMY_VAR__);
-                        stan::math::assign(nu,(get_base1(regularization,pos_reg,"regularization",1) + (0.5 * (get_base1(p,i,"p",1) - 2))));
-
-
-                        current_statement_begin__ = 530;
-                        stan::math::assign(pos_reg, (pos_reg + 1));
-                        current_statement_begin__ = 531;
-                        stan::math::assign(get_base1_lhs(shape1,1,"shape1",1), nu);
-                        current_statement_begin__ = 532;
-                        stan::math::assign(get_base1_lhs(shape2,1,"shape2",1), nu);
-                        current_statement_begin__ = 533;
-                        for (int j = 2; j <= (get_base1(p,i,"p",1) - 1); ++j) {
-
-                            current_statement_begin__ = 534;
-                            stan::math::assign(nu, (nu - 0.5));
-                            current_statement_begin__ = 535;
-                            stan::math::assign(get_base1_lhs(shape1,j,"shape1",1), (0.5 * j));
-                            current_statement_begin__ = 536;
-                            stan::math::assign(get_base1_lhs(shape2,j,"shape2",1), nu);
-                        }
-                        current_statement_begin__ = 538;
-                        lp_accum__.add(beta_log(stan::model::rvalue(rho, stan::model::cons_list(stan::model::index_min_max(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 2)), stan::model::nil_index_list()), "rho"),shape1,shape2));
-                        current_statement_begin__ = 539;
-                        stan::math::assign(pos_rho, ((pos_rho + get_base1(p,i,"p",1)) - 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 541;
-            lp_accum__.add(gamma_log(zeta,delta,1));
-            current_statement_begin__ = 542;
-            lp_accum__.add(gamma_log(tau,shape,1));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct decov_lp_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__, typename T5__, typename T6__, typename T7__, typename T_lp__, typename T_lp_accum__>
-        void
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_b,
-             const Eigen::Matrix<T1__, Eigen::Dynamic,1>& z_T,
-             const Eigen::Matrix<T2__, Eigen::Dynamic,1>& rho,
-             const Eigen::Matrix<T3__, Eigen::Dynamic,1>& zeta,
-             const Eigen::Matrix<T4__, Eigen::Dynamic,1>& tau,
-             const std::vector<T5__>& regularization,
-             const std::vector<T6__>& delta,
-             const Eigen::Matrix<T7__, Eigen::Dynamic,1>& shape,
-             const int& t,
-             const std::vector<int>& p, T_lp__& lp__, T_lp_accum__& lp_accum__, std::ostream* pstream__) const {
-        return decov_lp(z_b, z_T, rho, zeta, tau, regularization, delta, shape, t, p, lp__, lp_accum__, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hs_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("lambda", "rows(z_beta)", rows(z_beta));
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  lambda(static_cast<Eigen::VectorXd::Index>(rows(z_beta)));
-            (void) lambda;  // dummy to suppress unused var warning
-
-            stan::math::initialize(lambda, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(lambda,DUMMY_VAR__);
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-
-
-            current_statement_begin__ = 559;
-            stan::math::assign(K, rows(z_beta));
-            current_statement_begin__ = 560;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 560;
-                stan::math::assign(get_base1_lhs(lambda,k,"lambda",1), (get_base1(get_base1(local,1,"local",1),k,"local",2) * sqrt(get_base1(get_base1(local,2,"local",1),k,"local",2))));
-            }
-            current_statement_begin__ = 561;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(z_beta,lambda),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hs_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-             const std::vector<T1__>& global,
-             const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-             const T3__& global_prior_scale,
-             const T4__& error_scale, std::ostream* pstream__) const {
-        return hs_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-hsplus_prior(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 577;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(multiply(multiply(multiply(multiply(elt_multiply(elt_multiply(z_beta,elt_multiply(get_base1(local,1,"local",1),sqrt(get_base1(local,2,"local",1)))),elt_multiply(get_base1(local,3,"local",1),sqrt(get_base1(local,4,"local",1)))),get_base1(global,1,"global",1)),sqrt(get_base1(global,2,"global",1))),global_prior_scale),error_scale));
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct hsplus_prior_functor__ {
-    template <typename T0__, typename T1__, typename T2__, typename T3__, typename T4__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__, T2__, T3__, typename boost::math::tools::promote_args<T4__>::type>::type, Eigen::Dynamic,1>
-    operator()(const Eigen::Matrix<T0__, Eigen::Dynamic,1>& z_beta,
-                 const std::vector<T1__>& global,
-                 const std::vector<Eigen::Matrix<T2__, Eigen::Dynamic,1> >& local,
-                 const T3__& global_prior_scale,
-                 const T4__& error_scale, std::ostream* pstream__) const {
-        return hsplus_prior(z_beta, global, local, global_prior_scale, error_scale, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-divide_real_by_vector(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            int K(0);
-            (void) K;  // dummy to suppress unused var warning
-
-            stan::math::fill(K, std::numeric_limits<int>::min());
-            stan::math::assign(K,rows(y));
-            validate_non_negative_index("ret", "K", K);
-            Eigen::Matrix<fun_scalar_t__,Eigen::Dynamic,1>  ret(static_cast<Eigen::VectorXd::Index>(K));
-            (void) ret;  // dummy to suppress unused var warning
-
-            stan::math::initialize(ret, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(ret,DUMMY_VAR__);
-
-
-            current_statement_begin__ = 592;
-            for (int k = 1; k <= K; ++k) {
-                current_statement_begin__ = 592;
-                stan::math::assign(get_base1_lhs(ret,k,"ret",1), (x / get_base1(y,k,"y",1)));
-            }
-            current_statement_begin__ = 593;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(ret);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct divide_real_by_vector_functor__ {
-    template <typename T0__, typename T1__>
-        Eigen::Matrix<typename boost::math::tools::promote_args<T0__, T1__>::type, Eigen::Dynamic,1>
-    operator()(const T0__& x,
-                          const Eigen::Matrix<T1__, Eigen::Dynamic,1>& y, std::ostream* pstream__) const {
-        return divide_real_by_vector(x, y, pstream__);
-    }
-};
-
-template <typename T0__, typename T1__>
-typename boost::math::tools::promote_args<T0__, T1__>::type
-CFt(const T0__& z,
-        const T1__& df, std::ostream* pstream__) {
-    typedef typename boost::math::tools::promote_args<T0__, T1__>::type fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            fun_scalar_t__ z2;
-            (void) z2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z2,DUMMY_VAR__);
-            stan::math::assign(z2,square(z));
-            fun_scalar_t__ z3;
-            (void) z3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z3,DUMMY_VAR__);
-            stan::math::assign(z3,(z2 * z));
-            fun_scalar_t__ z5;
-            (void) z5;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z5, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z5,DUMMY_VAR__);
-            stan::math::assign(z5,(z2 * z3));
-            fun_scalar_t__ z7;
-            (void) z7;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z7, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z7,DUMMY_VAR__);
-            stan::math::assign(z7,(z2 * z5));
-            fun_scalar_t__ z9;
-            (void) z9;  // dummy to suppress unused var warning
-
-            stan::math::initialize(z9, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(z9,DUMMY_VAR__);
-            stan::math::assign(z9,(z2 * z7));
-            fun_scalar_t__ df2;
-            (void) df2;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df2, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df2,DUMMY_VAR__);
-            stan::math::assign(df2,square(df));
-            fun_scalar_t__ df3;
-            (void) df3;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df3, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df3,DUMMY_VAR__);
-            stan::math::assign(df3,(df2 * df));
-            fun_scalar_t__ df4;
-            (void) df4;  // dummy to suppress unused var warning
-
-            stan::math::initialize(df4, std::numeric_limits<double>::quiet_NaN());
-            stan::math::fill(df4,DUMMY_VAR__);
-            stan::math::assign(df4,(df2 * df2));
-
-
-            current_statement_begin__ = 615;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(((((z + ((z3 + z) / (4 * df))) + ((((5 * z5) + (16 * z3)) + (3 * z)) / (96 * df2))) + (((((3 * z7) + (19 * z5)) + (17 * z3)) - (15 * z)) / (384 * df3))) + ((((((79 * z9) + (776 * z7)) + (1482 * z5)) - (1920 * z3)) - (945 * z)) / (92160 * df4))));
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct CFt_functor__ {
-    template <typename T0__, typename T1__>
-        typename boost::math::tools::promote_args<T0__, T1__>::type
-    operator()(const T0__& z,
-        const T1__& df, std::ostream* pstream__) const {
-        return CFt(z, df, pstream__);
-    }
-};
-
-std::vector<std::vector<int> >
-make_V(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef int fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-        {
-            validate_non_negative_index("V", "t", t);
-            validate_non_negative_index("V", "N", N);
-            vector<vector<int> > V(t, (vector<int>(N, 0)));
-            stan::math::fill(V, std::numeric_limits<int>::min());
-            int pos(0);
-            (void) pos;  // dummy to suppress unused var warning
-
-            stan::math::fill(pos, std::numeric_limits<int>::min());
-            stan::math::assign(pos,1);
-
-
-            current_statement_begin__ = 630;
-            if (as_bool(logical_gt(t,0))) {
-                current_statement_begin__ = 630;
-                for (int j = 1; j <= N; ++j) {
-                    current_statement_begin__ = 630;
-                    for (int i = 1; i <= t; ++i) {
-
-                        current_statement_begin__ = 631;
-                        stan::math::assign(get_base1_lhs(get_base1_lhs(V,i,"V",1),j,"V",2), get_base1(v,pos,"v",1));
-                        current_statement_begin__ = 632;
-                        stan::math::assign(pos, (pos + 1));
-                    }
-                }
-            }
-            current_statement_begin__ = 634;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(V);
-        }
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_V_functor__ {
-            std::vector<std::vector<int> >
-    operator()(const int& N,
-           const int& t,
-           const std::vector<int>& v, std::ostream* pstream__) const {
-        return make_V(N, t, v, pstream__);
-    }
-};
-
-double
-make_lower(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 645;
-        if (as_bool(logical_eq(family,1))) {
-            current_statement_begin__ = 645;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-        }
-        current_statement_begin__ = 646;
-        if (as_bool(logical_eq(family,5))) {
-
-            current_statement_begin__ = 647;
-            if (as_bool(logical_eq(link,2))) {
-                current_statement_begin__ = 647;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            current_statement_begin__ = 648;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0);
-        }
-        current_statement_begin__ = 650;
-        if (as_bool((primitive_value(logical_eq(family,2)) || primitive_value(logical_eq(family,3))))) {
-
-            current_statement_begin__ = 651;
-            if (as_bool(logical_eq(link,1))) {
-                current_statement_begin__ = 651;
-                return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-            }
-            current_statement_begin__ = 652;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0.0);
-        }
-        current_statement_begin__ = 654;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::negative_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_lower_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_lower(family, link, pstream__);
-    }
-};
-
-double
-make_upper(const int& family,
-               const int& link, std::ostream* pstream__) {
-    typedef double fun_scalar_t__;
-    typedef fun_scalar_t__ fun_return_scalar_t__;
-    const static bool propto__ = true;
-    (void) propto__;
-        fun_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-    int current_statement_begin__ = -1;
-    try {
-
-        current_statement_begin__ = 665;
-        if (as_bool((primitive_value(logical_eq(family,4)) && primitive_value(logical_eq(link,4))))) {
-            current_statement_begin__ = 665;
-            return stan::math::promote_scalar<fun_return_scalar_t__>(0.0);
-        }
-        current_statement_begin__ = 666;
-        return stan::math::promote_scalar<fun_return_scalar_t__>(stan::math::positive_infinity());
-    } catch (const std::exception& e) {
-        stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-        // Next line prevents compiler griping about no return
-        throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-    }
-}
-
-
-struct make_upper_functor__ {
-            double
-    operator()(const int& family,
-               const int& link, std::ostream* pstream__) const {
-        return make_upper(family, link, pstream__);
-    }
-};
-
-class model_spatial : public prob_grad {
-private:
-    int N;
-    int K;
-    matrix_d X;
-    int family;
-    int link;
-    int is_continuous;
-    int has_aux;
-    int model_type;
-    int has_intercept;
-    vector_d xbar;
-    vector<int> trials;
-    vector<int> y_int;
-    vector_d y_real;
-    int E_n;
-    vector<vector<int> > edges;
-    int order;
-    vector<int> Q_n;
-    vector_d w;
-    vector<int> v;
-    vector<int> u;
-    int prior_dist_rho;
-    double prior_mean_rho;
-    double prior_scale_rho;
-    double prior_df_rho;
-    double shape1_rho;
-    double shape2_rho;
-    double scaling_factor;
-    int prior_dist_for_intercept;
-    int prior_dist;
-    int prior_dist_tau;
-    double prior_mean_for_intercept;
-    double prior_scale_for_intercept;
-    double prior_df_for_intercept;
-    vector_d prior_mean;
-    vector_d prior_scale;
-    vector_d prior_df;
-    double prior_mean_tau;
-    double prior_scale_tau;
-    double prior_df_tau;
-    double global_prior_df;
-    double global_prior_scale;
-    vector<int> num_normals;
-    int prior_dist_for_aux;
-    double prior_mean_for_aux;
-    double prior_scale_for_aux;
-    double prior_df_for_aux;
-    double poisson_max;
-    int hs;
-    double sum_log_y;
-public:
-    model_spatial(stan::io::var_context& context__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, 0, pstream__);
-    }
-
-    model_spatial(stan::io::var_context& context__,
-        unsigned int random_seed__,
-        std::ostream* pstream__ = 0)
-        : prob_grad(0) {
-        ctor_body(context__, random_seed__, pstream__);
-    }
-
-    void ctor_body(stan::io::var_context& context__,
-                   unsigned int random_seed__,
-                   std::ostream* pstream__) {
-        boost::ecuyer1988 base_rng__ =
-          stan::services::util::create_rng(random_seed__, 0);
-        (void) base_rng__;  // suppress unused var warning
-
-        current_statement_begin__ = -1;
-
-        static const char* function__ = "model_spatial_namespace::model_spatial";
-        (void) function__;  // dummy to suppress unused var warning
-        size_t pos__;
-        (void) pos__;  // dummy to suppress unused var warning
-        std::vector<int> vals_i__;
-        std::vector<double> vals_r__;
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        // initialize member variables
-        context__.validate_dims("data initialization", "N", "int", context__.to_vec());
-        N = int(0);
-        vals_i__ = context__.vals_i("N");
-        pos__ = 0;
-        N = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "K", "int", context__.to_vec());
-        K = int(0);
-        vals_i__ = context__.vals_i("K");
-        pos__ = 0;
-        K = vals_i__[pos__++];
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        context__.validate_dims("data initialization", "X", "matrix_d", context__.to_vec(N,K));
-        validate_non_negative_index("X", "N", N);
-        validate_non_negative_index("X", "K", K);
-        X = matrix_d(static_cast<Eigen::VectorXd::Index>(N),static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("X");
-        pos__ = 0;
-        size_t X_m_mat_lim__ = N;
-        size_t X_n_mat_lim__ = K;
-        for (size_t n_mat__ = 0; n_mat__ < X_n_mat_lim__; ++n_mat__) {
-            for (size_t m_mat__ = 0; m_mat__ < X_m_mat_lim__; ++m_mat__) {
-                X(m_mat__,n_mat__) = vals_r__[pos__++];
-            }
-        }
-        context__.validate_dims("data initialization", "family", "int", context__.to_vec());
-        family = int(0);
-        vals_i__ = context__.vals_i("family");
-        pos__ = 0;
-        family = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "link", "int", context__.to_vec());
-        link = int(0);
-        vals_i__ = context__.vals_i("link");
-        pos__ = 0;
-        link = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "is_continuous", "int", context__.to_vec());
-        is_continuous = int(0);
-        vals_i__ = context__.vals_i("is_continuous");
-        pos__ = 0;
-        is_continuous = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_aux", "int", context__.to_vec());
-        has_aux = int(0);
-        vals_i__ = context__.vals_i("has_aux");
-        pos__ = 0;
-        has_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "model_type", "int", context__.to_vec());
-        model_type = int(0);
-        vals_i__ = context__.vals_i("model_type");
-        pos__ = 0;
-        model_type = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "has_intercept", "int", context__.to_vec());
-        has_intercept = int(0);
-        vals_i__ = context__.vals_i("has_intercept");
-        pos__ = 0;
-        has_intercept = vals_i__[pos__++];
-        validate_non_negative_index("xbar", "K", K);
-        context__.validate_dims("data initialization", "xbar", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("xbar", "K", K);
-        xbar = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("xbar");
-        pos__ = 0;
-        size_t xbar_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < xbar_i_vec_lim__; ++i_vec__) {
-            xbar[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("trials", "(logical_eq(family,4) ? N : 0 )", (logical_eq(family,4) ? N : 0 ));
-        context__.validate_dims("data initialization", "trials", "int", context__.to_vec((logical_eq(family,4) ? N : 0 )));
-        validate_non_negative_index("trials", "(logical_eq(family,4) ? N : 0 )", (logical_eq(family,4) ? N : 0 ));
-        trials = std::vector<int>((logical_eq(family,4) ? N : 0 ),int(0));
-        vals_i__ = context__.vals_i("trials");
-        pos__ = 0;
-        size_t trials_limit_0__ = (logical_eq(family,4) ? N : 0 );
-        for (size_t i_0__ = 0; i_0__ < trials_limit_0__; ++i_0__) {
-            trials[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y_int", "(logical_eq(is_continuous,1) ? 0 : N )", (logical_eq(is_continuous,1) ? 0 : N ));
-        context__.validate_dims("data initialization", "y_int", "int", context__.to_vec((logical_eq(is_continuous,1) ? 0 : N )));
-        validate_non_negative_index("y_int", "(logical_eq(is_continuous,1) ? 0 : N )", (logical_eq(is_continuous,1) ? 0 : N ));
-        y_int = std::vector<int>((logical_eq(is_continuous,1) ? 0 : N ),int(0));
-        vals_i__ = context__.vals_i("y_int");
-        pos__ = 0;
-        size_t y_int_limit_0__ = (logical_eq(is_continuous,1) ? 0 : N );
-        for (size_t i_0__ = 0; i_0__ < y_int_limit_0__; ++i_0__) {
-            y_int[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("y_real", "(logical_eq(is_continuous,1) ? N : 0 )", (logical_eq(is_continuous,1) ? N : 0 ));
-        context__.validate_dims("data initialization", "y_real", "vector_d", context__.to_vec((logical_eq(is_continuous,1) ? N : 0 )));
-        validate_non_negative_index("y_real", "(logical_eq(is_continuous,1) ? N : 0 )", (logical_eq(is_continuous,1) ? N : 0 ));
-        y_real = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(is_continuous,1) ? N : 0 )));
-        vals_r__ = context__.vals_r("y_real");
-        pos__ = 0;
-        size_t y_real_i_vec_lim__ = (logical_eq(is_continuous,1) ? N : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < y_real_i_vec_lim__; ++i_vec__) {
-            y_real[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "E_n", "int", context__.to_vec());
-        E_n = int(0);
-        vals_i__ = context__.vals_i("E_n");
-        pos__ = 0;
-        E_n = vals_i__[pos__++];
-        validate_non_negative_index("edges", "E_n", E_n);
-        validate_non_negative_index("edges", "2", 2);
-        context__.validate_dims("data initialization", "edges", "int", context__.to_vec(E_n,2));
-        validate_non_negative_index("edges", "E_n", E_n);
-        validate_non_negative_index("edges", "2", 2);
-        edges = std::vector<std::vector<int> >(E_n,std::vector<int>(2,int(0)));
-        vals_i__ = context__.vals_i("edges");
-        pos__ = 0;
-        size_t edges_limit_1__ = 2;
-        for (size_t i_1__ = 0; i_1__ < edges_limit_1__; ++i_1__) {
-            size_t edges_limit_0__ = E_n;
-            for (size_t i_0__ = 0; i_0__ < edges_limit_0__; ++i_0__) {
-                edges[i_0__][i_1__] = vals_i__[pos__++];
-            }
-        }
-        context__.validate_dims("data initialization", "order", "int", context__.to_vec());
-        order = int(0);
-        vals_i__ = context__.vals_i("order");
-        pos__ = 0;
-        order = vals_i__[pos__++];
-        validate_non_negative_index("Q_n", "logical_eq(order,2)", logical_eq(order,2));
-        context__.validate_dims("data initialization", "Q_n", "int", context__.to_vec(logical_eq(order,2)));
-        validate_non_negative_index("Q_n", "logical_eq(order,2)", logical_eq(order,2));
-        Q_n = std::vector<int>(logical_eq(order,2),int(0));
-        vals_i__ = context__.vals_i("Q_n");
-        pos__ = 0;
-        size_t Q_n_limit_0__ = logical_eq(order,2);
-        for (size_t i_0__ = 0; i_0__ < Q_n_limit_0__; ++i_0__) {
-            Q_n[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("w", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        context__.validate_dims("data initialization", "w", "vector_d", context__.to_vec((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        validate_non_negative_index("w", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        w = vector_d(static_cast<Eigen::VectorXd::Index>((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        vals_r__ = context__.vals_r("w");
-        pos__ = 0;
-        size_t w_i_vec_lim__ = (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 );
-        for (size_t i_vec__ = 0; i_vec__ < w_i_vec_lim__; ++i_vec__) {
-            w[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("v", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        context__.validate_dims("data initialization", "v", "int", context__.to_vec((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 )));
-        validate_non_negative_index("v", "(logical_eq(order,2) ? get_base1(Q_n,1,\"Q_n\",1) : 0 )", (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ));
-        v = std::vector<int>((logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("v");
-        pos__ = 0;
-        size_t v_limit_0__ = (logical_eq(order,2) ? get_base1(Q_n,1,"Q_n",1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < v_limit_0__; ++i_0__) {
-            v[i_0__] = vals_i__[pos__++];
-        }
-        validate_non_negative_index("u", "(logical_eq(order,2) ? (N + 1) : 0 )", (logical_eq(order,2) ? (N + 1) : 0 ));
-        context__.validate_dims("data initialization", "u", "int", context__.to_vec((logical_eq(order,2) ? (N + 1) : 0 )));
-        validate_non_negative_index("u", "(logical_eq(order,2) ? (N + 1) : 0 )", (logical_eq(order,2) ? (N + 1) : 0 ));
-        u = std::vector<int>((logical_eq(order,2) ? (N + 1) : 0 ),int(0));
-        vals_i__ = context__.vals_i("u");
-        pos__ = 0;
-        size_t u_limit_0__ = (logical_eq(order,2) ? (N + 1) : 0 );
-        for (size_t i_0__ = 0; i_0__ < u_limit_0__; ++i_0__) {
-            u[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_rho", "int", context__.to_vec());
-        prior_dist_rho = int(0);
-        vals_i__ = context__.vals_i("prior_dist_rho");
-        pos__ = 0;
-        prior_dist_rho = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_rho", "double", context__.to_vec());
-        prior_mean_rho = double(0);
-        vals_r__ = context__.vals_r("prior_mean_rho");
-        pos__ = 0;
-        prior_mean_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_rho", "double", context__.to_vec());
-        prior_scale_rho = double(0);
-        vals_r__ = context__.vals_r("prior_scale_rho");
-        pos__ = 0;
-        prior_scale_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_rho", "double", context__.to_vec());
-        prior_df_rho = double(0);
-        vals_r__ = context__.vals_r("prior_df_rho");
-        pos__ = 0;
-        prior_df_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "shape1_rho", "double", context__.to_vec());
-        shape1_rho = double(0);
-        vals_r__ = context__.vals_r("shape1_rho");
-        pos__ = 0;
-        shape1_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "shape2_rho", "double", context__.to_vec());
-        shape2_rho = double(0);
-        vals_r__ = context__.vals_r("shape2_rho");
-        pos__ = 0;
-        shape2_rho = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "scaling_factor", "double", context__.to_vec());
-        scaling_factor = double(0);
-        vals_r__ = context__.vals_r("scaling_factor");
-        pos__ = 0;
-        scaling_factor = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_for_intercept", "int", context__.to_vec());
-        prior_dist_for_intercept = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_intercept");
-        pos__ = 0;
-        prior_dist_for_intercept = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist", "int", context__.to_vec());
-        prior_dist = int(0);
-        vals_i__ = context__.vals_i("prior_dist");
-        pos__ = 0;
-        prior_dist = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_dist_tau", "int", context__.to_vec());
-        prior_dist_tau = int(0);
-        vals_i__ = context__.vals_i("prior_dist_tau");
-        pos__ = 0;
-        prior_dist_tau = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_intercept", "double", context__.to_vec());
-        prior_mean_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_intercept");
-        pos__ = 0;
-        prior_mean_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_intercept", "double", context__.to_vec());
-        prior_scale_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_intercept");
-        pos__ = 0;
-        prior_scale_for_intercept = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_intercept", "double", context__.to_vec());
-        prior_df_for_intercept = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_intercept");
-        pos__ = 0;
-        prior_df_for_intercept = vals_r__[pos__++];
-        validate_non_negative_index("prior_mean", "K", K);
-        context__.validate_dims("data initialization", "prior_mean", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_mean", "K", K);
-        prior_mean = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_mean");
-        pos__ = 0;
-        size_t prior_mean_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_mean_i_vec_lim__; ++i_vec__) {
-            prior_mean[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_scale", "K", K);
-        context__.validate_dims("data initialization", "prior_scale", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_scale", "K", K);
-        prior_scale = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_scale");
-        pos__ = 0;
-        size_t prior_scale_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_scale_i_vec_lim__; ++i_vec__) {
-            prior_scale[i_vec__] = vals_r__[pos__++];
-        }
-        validate_non_negative_index("prior_df", "K", K);
-        context__.validate_dims("data initialization", "prior_df", "vector_d", context__.to_vec(K));
-        validate_non_negative_index("prior_df", "K", K);
-        prior_df = vector_d(static_cast<Eigen::VectorXd::Index>(K));
-        vals_r__ = context__.vals_r("prior_df");
-        pos__ = 0;
-        size_t prior_df_i_vec_lim__ = K;
-        for (size_t i_vec__ = 0; i_vec__ < prior_df_i_vec_lim__; ++i_vec__) {
-            prior_df[i_vec__] = vals_r__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_mean_tau", "double", context__.to_vec());
-        prior_mean_tau = double(0);
-        vals_r__ = context__.vals_r("prior_mean_tau");
-        pos__ = 0;
-        prior_mean_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_tau", "double", context__.to_vec());
-        prior_scale_tau = double(0);
-        vals_r__ = context__.vals_r("prior_scale_tau");
-        pos__ = 0;
-        prior_scale_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_tau", "double", context__.to_vec());
-        prior_df_tau = double(0);
-        vals_r__ = context__.vals_r("prior_df_tau");
-        pos__ = 0;
-        prior_df_tau = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_df", "double", context__.to_vec());
-        global_prior_df = double(0);
-        vals_r__ = context__.vals_r("global_prior_df");
-        pos__ = 0;
-        global_prior_df = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "global_prior_scale", "double", context__.to_vec());
-        global_prior_scale = double(0);
-        vals_r__ = context__.vals_r("global_prior_scale");
-        pos__ = 0;
-        global_prior_scale = vals_r__[pos__++];
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        context__.validate_dims("data initialization", "num_normals", "int", context__.to_vec((logical_eq(prior_dist,7) ? K : 0 )));
-        validate_non_negative_index("num_normals", "(logical_eq(prior_dist,7) ? K : 0 )", (logical_eq(prior_dist,7) ? K : 0 ));
-        num_normals = std::vector<int>((logical_eq(prior_dist,7) ? K : 0 ),int(0));
-        vals_i__ = context__.vals_i("num_normals");
-        pos__ = 0;
-        size_t num_normals_limit_0__ = (logical_eq(prior_dist,7) ? K : 0 );
-        for (size_t i_0__ = 0; i_0__ < num_normals_limit_0__; ++i_0__) {
-            num_normals[i_0__] = vals_i__[pos__++];
-        }
-        context__.validate_dims("data initialization", "prior_dist_for_aux", "int", context__.to_vec());
-        prior_dist_for_aux = int(0);
-        vals_i__ = context__.vals_i("prior_dist_for_aux");
-        pos__ = 0;
-        prior_dist_for_aux = vals_i__[pos__++];
-        context__.validate_dims("data initialization", "prior_mean_for_aux", "double", context__.to_vec());
-        prior_mean_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_mean_for_aux");
-        pos__ = 0;
-        prior_mean_for_aux = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_scale_for_aux", "double", context__.to_vec());
-        prior_scale_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_scale_for_aux");
-        pos__ = 0;
-        prior_scale_for_aux = vals_r__[pos__++];
-        context__.validate_dims("data initialization", "prior_df_for_aux", "double", context__.to_vec());
-        prior_df_for_aux = double(0);
-        vals_r__ = context__.vals_r("prior_df_for_aux");
-        pos__ = 0;
-        prior_df_for_aux = vals_r__[pos__++];
-
-        // validate, data variables
-        check_greater_or_equal(function__,"N",N,0);
-        check_greater_or_equal(function__,"K",K,0);
-        check_greater_or_equal(function__,"family",family,1);
-        check_less_or_equal(function__,"family",family,5);
-        check_greater_or_equal(function__,"is_continuous",is_continuous,0);
-        check_less_or_equal(function__,"is_continuous",is_continuous,1);
-        check_greater_or_equal(function__,"has_aux",has_aux,0);
-        check_less_or_equal(function__,"has_aux",has_aux,1);
-        check_greater_or_equal(function__,"model_type",model_type,1);
-        check_less_or_equal(function__,"model_type",model_type,3);
-        check_greater_or_equal(function__,"has_intercept",has_intercept,0);
-        check_less_or_equal(function__,"has_intercept",has_intercept,1);
-        for (int k0__ = 0; k0__ < (logical_eq(family,4) ? N : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"trials[k0__]",trials[k0__],0);
-        }
-        check_greater_or_equal(function__,"order",order,1);
-        check_less_or_equal(function__,"order",order,2);
-        for (int k0__ = 0; k0__ < logical_eq(order,2); ++k0__) {
-            check_greater_or_equal(function__,"Q_n[k0__]",Q_n[k0__],0);
-        }
-        check_greater_or_equal(function__,"prior_dist_rho",prior_dist_rho,0);
-        check_less_or_equal(function__,"prior_dist_rho",prior_dist_rho,1);
-        check_greater_or_equal(function__,"prior_scale_rho",prior_scale_rho,0);
-        check_greater_or_equal(function__,"prior_df_rho",prior_df_rho,0);
-        check_greater_or_equal(function__,"shape1_rho",shape1_rho,0);
-        check_greater_or_equal(function__,"shape2_rho",shape2_rho,0);
-        check_greater_or_equal(function__,"prior_dist_for_intercept",prior_dist_for_intercept,0);
-        check_greater_or_equal(function__,"prior_dist",prior_dist,0);
-        check_greater_or_equal(function__,"prior_dist_tau",prior_dist_tau,0);
-        check_greater_or_equal(function__,"prior_scale_for_intercept",prior_scale_for_intercept,0);
-        check_greater_or_equal(function__,"prior_df_for_intercept",prior_df_for_intercept,0);
-        check_greater_or_equal(function__,"prior_scale",prior_scale,0);
-        check_greater_or_equal(function__,"prior_df",prior_df,0);
-        check_greater_or_equal(function__,"prior_scale_tau",prior_scale_tau,0);
-        check_greater_or_equal(function__,"prior_df_tau",prior_df_tau,0);
-        check_greater_or_equal(function__,"global_prior_df",global_prior_df,0);
-        check_greater_or_equal(function__,"global_prior_scale",global_prior_scale,0);
-        for (int k0__ = 0; k0__ < (logical_eq(prior_dist,7) ? K : 0 ); ++k0__) {
-            check_greater_or_equal(function__,"num_normals[k0__]",num_normals[k0__],2);
-        }
-        check_greater_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,0);
-        check_less_or_equal(function__,"prior_dist_for_aux",prior_dist_for_aux,3);
-        check_greater_or_equal(function__,"prior_mean_for_aux",prior_mean_for_aux,0);
-        check_greater_or_equal(function__,"prior_scale_for_aux",prior_scale_for_aux,0);
-        check_greater_or_equal(function__,"prior_df_for_aux",prior_df_for_aux,0);
-        // initialize data variables
-        poisson_max = double(0);
-        stan::math::fill(poisson_max,DUMMY_VAR__);
-        stan::math::assign(poisson_max,pow(2.0,30.0));
-        hs = int(0);
-        stan::math::fill(hs, std::numeric_limits<int>::min());
-        sum_log_y = double(0);
-        stan::math::fill(sum_log_y,DUMMY_VAR__);
-        stan::math::assign(sum_log_y,(logical_eq(family,1) ? stan::math::not_a_number() : sum(log(y_real)) ));
-
-        try {
-            current_statement_begin__ = 724;
-            if (as_bool(logical_lte(prior_dist,2))) {
-                current_statement_begin__ = 724;
-                stan::math::assign(hs, 0);
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-                current_statement_begin__ = 725;
-                stan::math::assign(hs, 2);
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-                current_statement_begin__ = 726;
-                stan::math::assign(hs, 4);
-            } else {
-                current_statement_begin__ = 727;
-                stan::math::assign(hs, 0);
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed data
-        check_greater_or_equal(function__,"hs",hs,0);
-
-        // validate, set parameter ranges
-        num_params_r__ = 0U;
-        param_ranges_i__.clear();
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        num_params_r__ += has_intercept;
-        validate_non_negative_index("z_beta", "K", K);
-        num_params_r__ += K;
-        validate_non_negative_index("theta_raw", "(logical_eq(model_type,1) ? 0 : N )", (logical_eq(model_type,1) ? 0 : N ));
-        num_params_r__ += (logical_eq(model_type,1) ? 0 : N );
-        validate_non_negative_index("phi_raw", "(N - 1)", (N - 1));
-        num_params_r__ += (N - 1);
-        validate_non_negative_index("rho", "logical_neq(model_type,1)", logical_neq(model_type,1));
-        num_params_r__ += logical_neq(model_type,1);
-        ++num_params_r__;
-        validate_non_negative_index("global", "hs", hs);
-        num_params_r__ += hs;
-        validate_non_negative_index("local", "K", K);
-        validate_non_negative_index("local", "hs", hs);
-        num_params_r__ += K * hs;
-        validate_non_negative_index("mix", "K", K);
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        num_params_r__ += K * (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        validate_non_negative_index("aux_unscaled", "has_aux", has_aux);
-        num_params_r__ += has_aux;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        num_params_r__ += logical_eq(prior_dist,6);
-    }
-
-    ~model_spatial() { }
-
-
-    void transform_inits(const stan::io::var_context& context__,
-                         std::vector<int>& params_i__,
-                         std::vector<double>& params_r__,
-                         std::ostream* pstream__) const {
-        stan::io::writer<double> writer__(params_r__,params_i__);
-        size_t pos__;
-        (void) pos__; // dummy call to supress warning
-        std::vector<double> vals_r__;
-        std::vector<int> vals_i__;
-
-        if (!(context__.contains_r("gamma")))
-            throw std::runtime_error("variable gamma missing");
-        vals_r__ = context__.vals_r("gamma");
-        pos__ = 0U;
-        validate_non_negative_index("gamma", "has_intercept", has_intercept);
-        context__.validate_dims("initialization", "gamma", "double", context__.to_vec(has_intercept));
-        // generate_declaration gamma
-        std::vector<double> gamma(has_intercept,double(0));
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            gamma[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_intercept; ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),gamma[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable gamma: ") + e.what());
-        }
-
-        if (!(context__.contains_r("z_beta")))
-            throw std::runtime_error("variable z_beta missing");
-        vals_r__ = context__.vals_r("z_beta");
-        pos__ = 0U;
-        validate_non_negative_index("z_beta", "K", K);
-        context__.validate_dims("initialization", "z_beta", "vector_d", context__.to_vec(K));
-        // generate_declaration z_beta
-        vector_d z_beta(static_cast<Eigen::VectorXd::Index>(K));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            z_beta(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(z_beta);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable z_beta: ") + e.what());
-        }
-
-        if (!(context__.contains_r("theta_raw")))
-            throw std::runtime_error("variable theta_raw missing");
-        vals_r__ = context__.vals_r("theta_raw");
-        pos__ = 0U;
-        validate_non_negative_index("theta_raw", "(logical_eq(model_type,1) ? 0 : N )", (logical_eq(model_type,1) ? 0 : N ));
-        context__.validate_dims("initialization", "theta_raw", "vector_d", context__.to_vec((logical_eq(model_type,1) ? 0 : N )));
-        // generate_declaration theta_raw
-        vector_d theta_raw(static_cast<Eigen::VectorXd::Index>((logical_eq(model_type,1) ? 0 : N )));
-        for (int j1__ = 0U; j1__ < (logical_eq(model_type,1) ? 0 : N ); ++j1__)
-            theta_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(theta_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable theta_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("phi_raw")))
-            throw std::runtime_error("variable phi_raw missing");
-        vals_r__ = context__.vals_r("phi_raw");
-        pos__ = 0U;
-        validate_non_negative_index("phi_raw", "(N - 1)", (N - 1));
-        context__.validate_dims("initialization", "phi_raw", "vector_d", context__.to_vec((N - 1)));
-        // generate_declaration phi_raw
-        vector_d phi_raw(static_cast<Eigen::VectorXd::Index>((N - 1)));
-        for (int j1__ = 0U; j1__ < (N - 1); ++j1__)
-            phi_raw(j1__) = vals_r__[pos__++];
-        try {
-            writer__.vector_unconstrain(phi_raw);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable phi_raw: ") + e.what());
-        }
-
-        if (!(context__.contains_r("rho")))
-            throw std::runtime_error("variable rho missing");
-        vals_r__ = context__.vals_r("rho");
-        pos__ = 0U;
-        validate_non_negative_index("rho", "logical_neq(model_type,1)", logical_neq(model_type,1));
-        context__.validate_dims("initialization", "rho", "double", context__.to_vec(logical_neq(model_type,1)));
-        // generate_declaration rho
-        std::vector<double> rho(logical_neq(model_type,1),double(0));
-        for (int i0__ = 0U; i0__ < logical_neq(model_type,1); ++i0__)
-            rho[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_neq(model_type,1); ++i0__)
-            try {
-            writer__.scalar_lub_unconstrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) ),rho[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable rho: ") + e.what());
-        }
-
-        if (!(context__.contains_r("tau")))
-            throw std::runtime_error("variable tau missing");
-        vals_r__ = context__.vals_r("tau");
-        pos__ = 0U;
-        context__.validate_dims("initialization", "tau", "double", context__.to_vec());
-        // generate_declaration tau
-        double tau(0);
-        tau = vals_r__[pos__++];
-        try {
-            writer__.scalar_lb_unconstrain(0,tau);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable tau: ") + e.what());
-        }
-
-        if (!(context__.contains_r("global")))
-            throw std::runtime_error("variable global missing");
-        vals_r__ = context__.vals_r("global");
-        pos__ = 0U;
-        validate_non_negative_index("global", "hs", hs);
-        context__.validate_dims("initialization", "global", "double", context__.to_vec(hs));
-        // generate_declaration global
-        std::vector<double> global(hs,double(0));
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            global[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,global[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable global: ") + e.what());
-        }
-
-        if (!(context__.contains_r("local")))
-            throw std::runtime_error("variable local missing");
-        vals_r__ = context__.vals_r("local");
-        pos__ = 0U;
-        validate_non_negative_index("local", "hs", hs);
-        validate_non_negative_index("local", "K", K);
-        context__.validate_dims("initialization", "local", "vector_d", context__.to_vec(hs,K));
-        // generate_declaration local
-        std::vector<vector_d> local(hs,vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < hs; ++i0__)
-                local[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < hs; ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,local[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable local: ") + e.what());
-        }
-
-        if (!(context__.contains_r("mix")))
-            throw std::runtime_error("variable mix missing");
-        vals_r__ = context__.vals_r("mix");
-        pos__ = 0U;
-        validate_non_negative_index("mix", "(primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)))", (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        validate_non_negative_index("mix", "K", K);
-        context__.validate_dims("initialization", "mix", "vector_d", context__.to_vec((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),K));
-        // generate_declaration mix
-        std::vector<vector_d> mix((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))),vector_d(static_cast<Eigen::VectorXd::Index>(K)));
-        for (int j1__ = 0U; j1__ < K; ++j1__)
-            for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-                mix[i0__](j1__) = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++i0__)
-            try {
-            writer__.vector_lb_unconstrain(0,mix[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable mix: ") + e.what());
-        }
-
-        if (!(context__.contains_r("aux_unscaled")))
-            throw std::runtime_error("variable aux_unscaled missing");
-        vals_r__ = context__.vals_r("aux_unscaled");
-        pos__ = 0U;
-        validate_non_negative_index("aux_unscaled", "has_aux", has_aux);
-        context__.validate_dims("initialization", "aux_unscaled", "double", context__.to_vec(has_aux));
-        // generate_declaration aux_unscaled
-        std::vector<double> aux_unscaled(has_aux,double(0));
-        for (int i0__ = 0U; i0__ < has_aux; ++i0__)
-            aux_unscaled[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < has_aux; ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,aux_unscaled[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable aux_unscaled: ") + e.what());
-        }
-
-        if (!(context__.contains_r("one_over_lambda")))
-            throw std::runtime_error("variable one_over_lambda missing");
-        vals_r__ = context__.vals_r("one_over_lambda");
-        pos__ = 0U;
-        validate_non_negative_index("one_over_lambda", "logical_eq(prior_dist,6)", logical_eq(prior_dist,6));
-        context__.validate_dims("initialization", "one_over_lambda", "double", context__.to_vec(logical_eq(prior_dist,6)));
-        // generate_declaration one_over_lambda
-        std::vector<double> one_over_lambda(logical_eq(prior_dist,6),double(0));
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            one_over_lambda[i0__] = vals_r__[pos__++];
-        for (int i0__ = 0U; i0__ < logical_eq(prior_dist,6); ++i0__)
-            try {
-            writer__.scalar_lb_unconstrain(0,one_over_lambda[i0__]);
-        } catch (const std::exception& e) { 
-            throw std::runtime_error(std::string("Error transforming variable one_over_lambda: ") + e.what());
-        }
-
-        params_r__ = writer__.data_r();
-        params_i__ = writer__.data_i();
-    }
-
-    void transform_inits(const stan::io::var_context& context,
-                         Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                         std::ostream* pstream__) const {
-      std::vector<double> params_r_vec;
-      std::vector<int> params_i_vec;
-      transform_inits(context, params_i_vec, params_r_vec, pstream__);
-      params_r.resize(params_r_vec.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r(i) = params_r_vec[i];
-    }
-
-
-    template <bool propto__, bool jacobian__, typename T__>
-    T__ log_prob(vector<T__>& params_r__,
-                 vector<int>& params_i__,
-                 std::ostream* pstream__ = 0) const {
-
-        T__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        T__ lp__(0.0);
-        stan::math::accumulator<T__> lp_accum__;
-
-        // model parameters
-        stan::io::reader<T__> in__(params_r__,params_i__);
-
-        vector<T__> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        gamma.reserve(dim_gamma_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            if (jacobian__)
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__),lp__));
-            else
-                gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  z_beta;
-        (void) z_beta;  // dummy to suppress unused var warning
-        if (jacobian__)
-            z_beta = in__.vector_constrain(K,lp__);
-        else
-            z_beta = in__.vector_constrain(K);
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  theta_raw;
-        (void) theta_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ),lp__);
-        else
-            theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ));
-
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  phi_raw;
-        (void) phi_raw;  // dummy to suppress unused var warning
-        if (jacobian__)
-            phi_raw = in__.vector_constrain((N - 1),lp__);
-        else
-            phi_raw = in__.vector_constrain((N - 1));
-
-        vector<T__> rho;
-        size_t dim_rho_0__ = logical_neq(model_type,1);
-        rho.reserve(dim_rho_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_rho_0__; ++k_0__) {
-            if (jacobian__)
-                rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) ),lp__));
-            else
-                rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) )));
-        }
-
-        T__ tau;
-        (void) tau;  // dummy to suppress unused var warning
-        if (jacobian__)
-            tau = in__.scalar_lb_constrain(0,lp__);
-        else
-            tau = in__.scalar_lb_constrain(0);
-
-        vector<T__> global;
-        size_t dim_global_0__ = hs;
-        global.reserve(dim_global_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            if (jacobian__)
-                global.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                global.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > local;
-        size_t dim_local_0__ = hs;
-        local.reserve(dim_local_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            if (jacobian__)
-                local.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                local.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<Eigen::Matrix<T__,Eigen::Dynamic,1> > mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        mix.reserve(dim_mix_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            if (jacobian__)
-                mix.push_back(in__.vector_lb_constrain(0,K,lp__));
-            else
-                mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-
-        vector<T__> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = has_aux;
-        aux_unscaled.reserve(dim_aux_unscaled_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            if (jacobian__)
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-
-        vector<T__> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        one_over_lambda.reserve(dim_one_over_lambda_0__);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            if (jacobian__)
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0,lp__));
-            else
-                one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-
-
-        // transformed parameters
-        validate_non_negative_index("beta", "K", K);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, DUMMY_VAR__);
-        stan::math::fill(beta,DUMMY_VAR__);
-        T__ aux;
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, DUMMY_VAR__);
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(has_aux,0) ? stan::math::promote_scalar<T__>(0) : stan::math::promote_scalar<T__>((logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<T__>(get_base1(aux_unscaled,1,"aux_unscaled",1)) : stan::math::promote_scalar<T__>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<T__>(((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)) + prior_mean_for_aux)) : stan::math::promote_scalar<T__>((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1))) )) )) ));
-        validate_non_negative_index("phi", "N", N);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  phi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) phi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(phi, DUMMY_VAR__);
-        stan::math::fill(phi,DUMMY_VAR__);
-        validate_non_negative_index("psi", "N", N);
-        Eigen::Matrix<T__,Eigen::Dynamic,1>  psi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) psi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(psi, DUMMY_VAR__);
-        stan::math::fill(psi,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 751;
-            stan::model::assign(phi, 
-                        stan::model::cons_list(stan::model::index_min_max(1, (N - 1)), stan::model::nil_index_list()), 
-                        phi_raw, 
-                        "assigning variable phi");
-            current_statement_begin__ = 752;
-            stan::math::assign(get_base1_lhs(phi,N,"phi",1), -(sum(phi_raw)));
-            current_statement_begin__ = 761;
-            if (as_bool(logical_eq(model_type,1))) {
-                current_statement_begin__ = 762;
-                stan::math::assign(psi, multiply(phi,tau));
-            } else if (as_bool(logical_eq(model_type,2))) {
-                current_statement_begin__ = 764;
-                stan::math::assign(psi, add(multiply(phi,get_base1(rho,1,"rho",1)),multiply(theta_raw,tau)));
-            } else if (as_bool(logical_eq(model_type,3))) {
-                current_statement_begin__ = 766;
-                stan::math::assign(psi, multiply(tau,add(multiply(sqrt((1 - get_base1(rho,1,"rho",1))),theta_raw),multiply(sqrt((get_base1(rho,1,"rho",1) / scaling_factor)),phi))));
-            }
-            current_statement_begin__ = 769;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 769;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 770;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 771;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 772;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 775;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 776;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 777;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 780;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 781;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 782;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 785;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 787;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 790;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 791;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 792;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 793;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 794;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 795;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 797;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-        for (int i0__ = 0; i0__ < K; ++i0__) {
-            if (stan::math::is_uninitialized(beta(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: beta" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        if (stan::math::is_uninitialized(aux)) {
-            std::stringstream msg__;
-            msg__ << "Undefined transformed parameter: aux";
-            throw std::runtime_error(msg__.str());
-        }
-        for (int i0__ = 0; i0__ < N; ++i0__) {
-            if (stan::math::is_uninitialized(phi(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: phi" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-        for (int i0__ = 0; i0__ < N; ++i0__) {
-            if (stan::math::is_uninitialized(psi(i0__))) {
-                std::stringstream msg__;
-                msg__ << "Undefined transformed parameter: psi" << '[' << i0__ << ']';
-                throw std::runtime_error(msg__.str());
-            }
-        }
-
-        const char* function__ = "validate transformed params";
-        (void) function__;  // dummy to suppress unused var warning
-
-        // model body
-        try {
-            {
-                validate_non_negative_index("eta", "N", N);
-                Eigen::Matrix<T__,Eigen::Dynamic,1>  eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, DUMMY_VAR__);
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 804;
-                if (as_bool(logical_eq(has_intercept,1))) {
-                    current_statement_begin__ = 805;
-                    stan::math::assign(eta, add(add(get_base1(gamma,1,"gamma",1),multiply(X,beta)),psi));
-                } else {
-                    current_statement_begin__ = 807;
-                    stan::math::assign(eta, add(multiply(X,beta),psi));
-                }
-                current_statement_begin__ = 809;
-                if (as_bool(logical_eq(family,1))) {
-
-                    current_statement_begin__ = 810;
-                    stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    current_statement_begin__ = 811;
-                    lp_accum__.add(normal_log(y_real,eta,aux));
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    current_statement_begin__ = 814;
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    current_statement_begin__ = 815;
-                    lp_accum__.add(poisson_log(y_int,eta));
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    current_statement_begin__ = 818;
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    current_statement_begin__ = 819;
-                    lp_accum__.add(neg_binomial_2_log(y_int,eta,aux));
-                } else if (as_bool(logical_eq(family,4))) {
-
-                    current_statement_begin__ = 822;
-                    stan::math::assign(eta, linkinv_binom(eta,link, pstream__));
-                    current_statement_begin__ = 823;
-                    lp_accum__.add(binomial_log(y_int,trials,eta));
-                } else if (as_bool(logical_eq(family,5))) {
-
-                    current_statement_begin__ = 826;
-                    lp_accum__.add(GammaReg(y_real,eta,aux,link,sum_log_y, pstream__));
-                }
-                current_statement_begin__ = 829;
-                if (as_bool(logical_eq(order,1))) {
-                    current_statement_begin__ = 830;
-                    lp_accum__.add((-(0.5) * dot_self(subtract(stan::model::rvalue(phi, stan::model::cons_list(stan::model::index_multi(stan::model::rvalue(edges, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(1), stan::model::nil_index_list())), "edges")), stan::model::nil_index_list()), "phi"),stan::model::rvalue(phi, stan::model::cons_list(stan::model::index_multi(stan::model::rvalue(edges, stan::model::cons_list(stan::model::index_omni(), stan::model::cons_list(stan::model::index_uni(2), stan::model::nil_index_list())), "edges")), stan::model::nil_index_list()), "phi")))));
-                } else if (as_bool(logical_eq(order,2))) {
-                    current_statement_begin__ = 832;
-                    lp_accum__.add((-(0.5) * dot_product(phi,csr_matrix_times_vector(N,N,w,v,u,phi))));
-                }
-                current_statement_begin__ = 836;
-                if (as_bool(logical_eq(prior_dist,1))) {
-                    current_statement_begin__ = 836;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,2))) {
-                    current_statement_begin__ = 837;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                } else if (as_bool(logical_eq(prior_dist,3))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 840;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 841;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 842;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 843;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 844;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,4))) {
-                    {
-                        T__ log_half;
-                        (void) log_half;  // dummy to suppress unused var warning
-
-                        stan::math::initialize(log_half, DUMMY_VAR__);
-                        stan::math::fill(log_half,DUMMY_VAR__);
-                        stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                        current_statement_begin__ = 848;
-                        lp_accum__.add(normal_log(z_beta,0,1));
-                        current_statement_begin__ = 849;
-                        lp_accum__.add((normal_log(get_base1(local,1,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 850;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,2,"local",1),multiply(0.5,prior_df),multiply(0.5,prior_df)));
-                        current_statement_begin__ = 851;
-                        lp_accum__.add((normal_log(get_base1(local,3,"local",1),0,1) - log_half));
-                        current_statement_begin__ = 853;
-                        lp_accum__.add(inv_gamma_log(get_base1(local,4,"local",1),multiply(0.5,prior_scale),multiply(0.5,prior_scale)));
-                        current_statement_begin__ = 854;
-                        lp_accum__.add((normal_log(get_base1(global,1,"global",1),0,1) - log_half));
-                        current_statement_begin__ = 855;
-                        lp_accum__.add(inv_gamma_log(get_base1(global,2,"global",1),(0.5 * global_prior_df),(0.5 * global_prior_df)));
-                    }
-                } else if (as_bool(logical_eq(prior_dist,5))) {
-
-                    current_statement_begin__ = 858;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 859;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                } else if (as_bool(logical_eq(prior_dist,6))) {
-
-                    current_statement_begin__ = 862;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                    current_statement_begin__ = 863;
-                    lp_accum__.add(exponential_log(get_base1(mix,1,"mix",1),1));
-                    current_statement_begin__ = 864;
-                    lp_accum__.add(chi_square_log(get_base1(one_over_lambda,1,"one_over_lambda",1),get_base1(prior_df,1,"prior_df",1)));
-                } else if (as_bool(logical_eq(prior_dist,7))) {
-
-                    current_statement_begin__ = 867;
-                    lp_accum__.add(normal_log(z_beta,0,1));
-                }
-                current_statement_begin__ = 872;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 873;
-                    if (as_bool(logical_eq(prior_dist_for_intercept,1))) {
-                        current_statement_begin__ = 874;
-                        lp_accum__.add(normal_log(gamma,prior_mean_for_intercept,prior_scale_for_intercept));
-                    } else if (as_bool(logical_eq(prior_dist_for_intercept,2))) {
-                        current_statement_begin__ = 876;
-                        lp_accum__.add(student_t_log(gamma,prior_df_for_intercept,prior_mean_for_intercept,prior_scale_for_intercept));
-                    }
-                }
-                current_statement_begin__ = 881;
-                if (as_bool(logical_eq(model_type,2))) {
-
-                    current_statement_begin__ = 882;
-                    lp_accum__.add(normal_log(theta_raw,0,1));
-                    current_statement_begin__ = 884;
-                    if (as_bool(logical_eq(prior_dist_rho,1))) {
-                        current_statement_begin__ = 885;
-                        lp_accum__.add(normal_log(get_base1(rho,1,"rho",1),prior_mean_rho,prior_scale_rho));
-                    } else if (as_bool(logical_eq(prior_dist_rho,2))) {
-                        current_statement_begin__ = 887;
-                        lp_accum__.add(student_t_log(get_base1(rho,1,"rho",1),prior_df_rho,prior_mean_rho,prior_scale_rho));
-                    } else if (as_bool(logical_eq(prior_dist_rho,3))) {
-                        current_statement_begin__ = 889;
-                        lp_accum__.add(exponential_log(get_base1(rho,1,"rho",1),prior_scale_rho));
-                    }
-                } else if (as_bool(logical_eq(model_type,3))) {
-
-                    current_statement_begin__ = 893;
-                    lp_accum__.add(normal_log(theta_raw,0,1));
-                    current_statement_begin__ = 894;
-                    if (as_bool(logical_eq(prior_dist_rho,1))) {
-                        current_statement_begin__ = 895;
-                        lp_accum__.add(beta_log(get_base1(rho,1,"rho",1),shape1_rho,shape2_rho));
-                    }
-                }
-                current_statement_begin__ = 899;
-                if (as_bool(logical_eq(prior_dist_tau,1))) {
-                    current_statement_begin__ = 900;
-                    lp_accum__.add(normal_log(tau,prior_mean_tau,prior_scale_tau));
-                } else if (as_bool(logical_eq(prior_dist_tau,2))) {
-                    current_statement_begin__ = 902;
-                    lp_accum__.add(student_t_log(tau,prior_df_tau,prior_mean_tau,prior_scale_tau));
-                } else if (as_bool(logical_eq(prior_dist_tau,3))) {
-                    current_statement_begin__ = 904;
-                    lp_accum__.add(exponential_log(tau,prior_scale_tau));
-                }
-                current_statement_begin__ = 907;
-                if (as_bool(logical_eq(has_aux,1))) {
-
-                    current_statement_begin__ = 910;
-                    if (as_bool((primitive_value(logical_gt(prior_dist_for_aux,0)) && primitive_value(logical_gt(prior_scale_for_aux,0))))) {
-                        {
-                            T__ log_half;
-                            (void) log_half;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(log_half, DUMMY_VAR__);
-                            stan::math::fill(log_half,DUMMY_VAR__);
-                            stan::math::assign(log_half,-(0.6931471805599454));
-
-
-                            current_statement_begin__ = 912;
-                            if (as_bool(logical_eq(prior_dist_for_aux,1))) {
-                                current_statement_begin__ = 913;
-                                lp_accum__.add((normal_log(aux_unscaled,0,1) - log_half));
-                            } else if (as_bool(logical_eq(prior_dist_for_aux,2))) {
-                                current_statement_begin__ = 915;
-                                lp_accum__.add((student_t_log(aux_unscaled,prior_df_for_aux,0,1) - log_half));
-                            } else {
-                                current_statement_begin__ = 917;
-                                lp_accum__.add(exponential_log(aux_unscaled,1));
-                            }
-                        }
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        lp_accum__.add(lp__);
-        return lp_accum__.sum();
-
-    } // log_prob()
-
-    template <bool propto, bool jacobian, typename T_>
-    T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
-               std::ostream* pstream = 0) const {
-      std::vector<T_> vec_params_r;
-      vec_params_r.reserve(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        vec_params_r.push_back(params_r(i));
-      std::vector<int> vec_params_i;
-      return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
-    }
-
-
-    void get_param_names(std::vector<std::string>& names__) const {
-        names__.resize(0);
-        names__.push_back("gamma");
-        names__.push_back("z_beta");
-        names__.push_back("theta_raw");
-        names__.push_back("phi_raw");
-        names__.push_back("rho");
-        names__.push_back("tau");
-        names__.push_back("global");
-        names__.push_back("local");
-        names__.push_back("mix");
-        names__.push_back("aux_unscaled");
-        names__.push_back("one_over_lambda");
-        names__.push_back("beta");
-        names__.push_back("aux");
-        names__.push_back("phi");
-        names__.push_back("psi");
-        names__.push_back("mean_PPD");
-        names__.push_back("alpha");
-    }
-
-
-    void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
-        dimss__.resize(0);
-        std::vector<size_t> dims__;
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((logical_eq(model_type,1) ? 0 : N ));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((N - 1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_neq(model_type,1));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(hs);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back((primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))));
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_aux);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(logical_eq(prior_dist,6));
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(K);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(N);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dimss__.push_back(dims__);
-        dims__.resize(0);
-        dims__.push_back(has_intercept);
-        dimss__.push_back(dims__);
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng__,
-                     std::vector<double>& params_r__,
-                     std::vector<int>& params_i__,
-                     std::vector<double>& vars__,
-                     bool include_tparams__ = true,
-                     bool include_gqs__ = true,
-                     std::ostream* pstream__ = 0) const {
-        vars__.resize(0);
-        stan::io::reader<double> in__(params_r__,params_i__);
-        static const char* function__ = "model_spatial_namespace::write_array";
-        (void) function__;  // dummy to suppress unused var warning
-        // read-transform, write parameters
-        vector<double> gamma;
-        size_t dim_gamma_0__ = has_intercept;
-        for (size_t k_0__ = 0; k_0__ < dim_gamma_0__; ++k_0__) {
-            gamma.push_back(in__.scalar_lub_constrain(make_lower(family,link, pstream__),make_upper(family,link, pstream__)));
-        }
-        vector_d z_beta = in__.vector_constrain(K);
-        vector_d theta_raw = in__.vector_constrain((logical_eq(model_type,1) ? 0 : N ));
-        vector_d phi_raw = in__.vector_constrain((N - 1));
-        vector<double> rho;
-        size_t dim_rho_0__ = logical_neq(model_type,1);
-        for (size_t k_0__ = 0; k_0__ < dim_rho_0__; ++k_0__) {
-            rho.push_back(in__.scalar_lub_constrain(0,(logical_eq(model_type,3) ? stan::math::promote_scalar<double>(1) : stan::math::promote_scalar<double>(stan::math::positive_infinity()) )));
-        }
-        double tau = in__.scalar_lb_constrain(0);
-        vector<double> global;
-        size_t dim_global_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_global_0__; ++k_0__) {
-            global.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<vector_d> local;
-        size_t dim_local_0__ = hs;
-        for (size_t k_0__ = 0; k_0__ < dim_local_0__; ++k_0__) {
-            local.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<vector_d> mix;
-        size_t dim_mix_0__ = (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6)));
-        for (size_t k_0__ = 0; k_0__ < dim_mix_0__; ++k_0__) {
-            mix.push_back(in__.vector_lb_constrain(0,K));
-        }
-        vector<double> aux_unscaled;
-        size_t dim_aux_unscaled_0__ = has_aux;
-        for (size_t k_0__ = 0; k_0__ < dim_aux_unscaled_0__; ++k_0__) {
-            aux_unscaled.push_back(in__.scalar_lb_constrain(0));
-        }
-        vector<double> one_over_lambda;
-        size_t dim_one_over_lambda_0__ = logical_eq(prior_dist,6);
-        for (size_t k_0__ = 0; k_0__ < dim_one_over_lambda_0__; ++k_0__) {
-            one_over_lambda.push_back(in__.scalar_lb_constrain(0));
-        }
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(gamma[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(z_beta[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            vars__.push_back(theta_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < (N - 1); ++k_0__) {
-            vars__.push_back(phi_raw[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_neq(model_type,1); ++k_0__) {
-            vars__.push_back(rho[k_0__]);
-        }
-        vars__.push_back(tau);
-        for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-            vars__.push_back(global[k_0__]);
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < hs; ++k_0__) {
-                vars__.push_back(local[k_0__][k_1__]);
-            }
-        }
-        for (int k_1__ = 0; k_1__ < K; ++k_1__) {
-            for (int k_0__ = 0; k_0__ < (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                vars__.push_back(mix[k_0__][k_1__]);
-            }
-        }
-        for (int k_0__ = 0; k_0__ < has_aux; ++k_0__) {
-            vars__.push_back(aux_unscaled[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < logical_eq(prior_dist,6); ++k_0__) {
-            vars__.push_back(one_over_lambda[k_0__]);
-        }
-
-        if (!include_tparams__) return;
-        // declare and define transformed parameters
-        double lp__ = 0.0;
-        (void) lp__;  // dummy to suppress unused var warning
-        stan::math::accumulator<double> lp_accum__;
-
-        double DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
-        (void) DUMMY_VAR__;  // suppress unused var warning
-
-        validate_non_negative_index("beta", "K", K);
-        vector_d beta(static_cast<Eigen::VectorXd::Index>(K));
-        (void) beta;  // dummy to suppress unused var warning
-
-        stan::math::initialize(beta, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(beta,DUMMY_VAR__);
-        double aux(0.0);
-        (void) aux;  // dummy to suppress unused var warning
-
-        stan::math::initialize(aux, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(aux,DUMMY_VAR__);
-        stan::math::assign(aux,(logical_eq(has_aux,0) ? stan::math::promote_scalar<double>(0) : stan::math::promote_scalar<double>((logical_eq(prior_dist_for_aux,0) ? stan::math::promote_scalar<double>(get_base1(aux_unscaled,1,"aux_unscaled",1)) : stan::math::promote_scalar<double>((logical_lte(prior_dist_for_aux,2) ? stan::math::promote_scalar<double>(((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1)) + prior_mean_for_aux)) : stan::math::promote_scalar<double>((prior_scale_for_aux * get_base1(aux_unscaled,1,"aux_unscaled",1))) )) )) ));
-        validate_non_negative_index("phi", "N", N);
-        vector_d phi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) phi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(phi, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(phi,DUMMY_VAR__);
-        validate_non_negative_index("psi", "N", N);
-        vector_d psi(static_cast<Eigen::VectorXd::Index>(N));
-        (void) psi;  // dummy to suppress unused var warning
-
-        stan::math::initialize(psi, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(psi,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 751;
-            stan::model::assign(phi, 
-                        stan::model::cons_list(stan::model::index_min_max(1, (N - 1)), stan::model::nil_index_list()), 
-                        phi_raw, 
-                        "assigning variable phi");
-            current_statement_begin__ = 752;
-            stan::math::assign(get_base1_lhs(phi,N,"phi",1), -(sum(phi_raw)));
-            current_statement_begin__ = 761;
-            if (as_bool(logical_eq(model_type,1))) {
-                current_statement_begin__ = 762;
-                stan::math::assign(psi, multiply(phi,tau));
-            } else if (as_bool(logical_eq(model_type,2))) {
-                current_statement_begin__ = 764;
-                stan::math::assign(psi, add(multiply(phi,get_base1(rho,1,"rho",1)),multiply(theta_raw,tau)));
-            } else if (as_bool(logical_eq(model_type,3))) {
-                current_statement_begin__ = 766;
-                stan::math::assign(psi, multiply(tau,add(multiply(sqrt((1 - get_base1(rho,1,"rho",1))),theta_raw),multiply(sqrt((get_base1(rho,1,"rho",1) / scaling_factor)),phi))));
-            }
-            current_statement_begin__ = 769;
-            if (as_bool(logical_eq(prior_dist,0))) {
-                current_statement_begin__ = 769;
-                stan::math::assign(beta, z_beta);
-            } else if (as_bool(logical_eq(prior_dist,1))) {
-                current_statement_begin__ = 770;
-                stan::math::assign(beta, add(elt_multiply(z_beta,prior_scale),prior_mean));
-            } else if (as_bool(logical_eq(prior_dist,2))) {
-                current_statement_begin__ = 771;
-                for (int k = 1; k <= K; ++k) {
-
-                    current_statement_begin__ = 772;
-                    stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((CFt(get_base1(z_beta,k,"z_beta",1),get_base1(prior_df,k,"prior_df",1), pstream__) * get_base1(prior_scale,k,"prior_scale",1)) + get_base1(prior_mean,k,"prior_mean",1)));
-                }
-            } else if (as_bool(logical_eq(prior_dist,3))) {
-
-                current_statement_begin__ = 775;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 776;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 777;
-                    stan::math::assign(beta, hs_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,4))) {
-
-                current_statement_begin__ = 780;
-                if (as_bool((primitive_value(logical_eq(is_continuous,1)) && primitive_value(logical_eq(family,1))))) {
-                    current_statement_begin__ = 781;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,aux, pstream__));
-                } else {
-                    current_statement_begin__ = 782;
-                    stan::math::assign(beta, hsplus_prior(z_beta,global,local,global_prior_scale,1, pstream__));
-                }
-            } else if (as_bool(logical_eq(prior_dist,5))) {
-                current_statement_begin__ = 785;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(prior_scale,sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,6))) {
-                current_statement_begin__ = 787;
-                stan::math::assign(beta, add(prior_mean,elt_multiply(elt_multiply(multiply(get_base1(one_over_lambda,1,"one_over_lambda",1),prior_scale),sqrt(multiply(2,get_base1(mix,1,"mix",1)))),z_beta)));
-            } else if (as_bool(logical_eq(prior_dist,7))) {
-                {
-                    int z_pos(0);
-                    (void) z_pos;  // dummy to suppress unused var warning
-
-                    stan::math::fill(z_pos, std::numeric_limits<int>::min());
-                    stan::math::assign(z_pos,1);
-
-
-                    current_statement_begin__ = 790;
-                    for (int k = 1; k <= K; ++k) {
-
-                        current_statement_begin__ = 791;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), get_base1(z_beta,z_pos,"z_beta",1));
-                        current_statement_begin__ = 792;
-                        stan::math::assign(z_pos, (z_pos + 1));
-                        current_statement_begin__ = 793;
-                        for (int n = 2; n <= get_base1(num_normals,k,"num_normals",1); ++n) {
-
-                            current_statement_begin__ = 794;
-                            stan::math::assign(get_base1_lhs(beta,k,"beta",1), (get_base1(beta,k,"beta",1) * get_base1(z_beta,z_pos,"z_beta",1)));
-                            current_statement_begin__ = 795;
-                            stan::math::assign(z_pos, (z_pos + 1));
-                        }
-                        current_statement_begin__ = 797;
-                        stan::math::assign(get_base1_lhs(beta,k,"beta",1), ((get_base1(beta,k,"beta",1) * pow(get_base1(prior_scale,k,"prior_scale",1),get_base1(num_normals,k,"num_normals",1))) + get_base1(prior_mean,k,"prior_mean",1)));
-                    }
-                }
-            }
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate transformed parameters
-
-        // write transformed parameters
-        for (int k_0__ = 0; k_0__ < K; ++k_0__) {
-            vars__.push_back(beta[k_0__]);
-        }
-        vars__.push_back(aux);
-        for (int k_0__ = 0; k_0__ < N; ++k_0__) {
-            vars__.push_back(phi[k_0__]);
-        }
-        for (int k_0__ = 0; k_0__ < N; ++k_0__) {
-            vars__.push_back(psi[k_0__]);
-        }
-
-        if (!include_gqs__) return;
-        // declare and define generated quantities
-        double mean_PPD(0.0);
-        (void) mean_PPD;  // dummy to suppress unused var warning
-
-        stan::math::initialize(mean_PPD, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(mean_PPD,DUMMY_VAR__);
-        stan::math::assign(mean_PPD,0);
-        validate_non_negative_index("alpha", "has_intercept", has_intercept);
-        vector<double> alpha(has_intercept, 0.0);
-        stan::math::initialize(alpha, std::numeric_limits<double>::quiet_NaN());
-        stan::math::fill(alpha,DUMMY_VAR__);
-
-
-        try {
-            current_statement_begin__ = 924;
-            if (as_bool(has_intercept)) {
-                current_statement_begin__ = 925;
-                stan::math::assign(get_base1_lhs(alpha,1,"alpha",1), (get_base1(gamma,1,"gamma",1) - dot_product(beta,xbar)));
-            }
-            {
-                validate_non_negative_index("eta", "N", N);
-                vector_d eta(static_cast<Eigen::VectorXd::Index>(N));
-                (void) eta;  // dummy to suppress unused var warning
-
-                stan::math::initialize(eta, std::numeric_limits<double>::quiet_NaN());
-                stan::math::fill(eta,DUMMY_VAR__);
-
-
-                current_statement_begin__ = 928;
-                if (as_bool(logical_eq(has_intercept,1))) {
-
-                    current_statement_begin__ = 929;
-                    stan::math::assign(eta, add(add(get_base1(alpha,1,"alpha",1),multiply(X,beta)),psi));
-                } else {
-
-                    current_statement_begin__ = 932;
-                    stan::math::assign(eta, add(multiply(X,beta),psi));
-                }
-                current_statement_begin__ = 934;
-                if (as_bool(logical_eq(family,1))) {
-
-                    current_statement_begin__ = 935;
-                    stan::math::assign(eta, linkinv_gauss(eta,link, pstream__));
-                    current_statement_begin__ = 936;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 936;
-                        stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),aux, base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,2))) {
-
-                    current_statement_begin__ = 939;
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    current_statement_begin__ = 940;
-                    for (int n = 1; n <= N; ++n) {
-
-                        current_statement_begin__ = 941;
-                        if (as_bool(logical_lt(get_base1(eta,n,"eta",1),poisson_max))) {
-                            current_statement_begin__ = 942;
-                            stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(get_base1(eta,n,"eta",1), base_rng__)));
-                        } else {
-                            current_statement_begin__ = 944;
-                            stan::math::assign(mean_PPD, (mean_PPD + normal_rng(get_base1(eta,n,"eta",1),sqrt(get_base1(eta,n,"eta",1)), base_rng__)));
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,3))) {
-
-                    current_statement_begin__ = 948;
-                    stan::math::assign(eta, linkinv_count(eta,link, pstream__));
-                    current_statement_begin__ = 949;
-                    for (int n = 1; n <= N; ++n) {
-                        {
-                            double gamma_temp(0.0);
-                            (void) gamma_temp;  // dummy to suppress unused var warning
-
-                            stan::math::initialize(gamma_temp, std::numeric_limits<double>::quiet_NaN());
-                            stan::math::fill(gamma_temp,DUMMY_VAR__);
-
-
-                            current_statement_begin__ = 951;
-                            if (as_bool(is_inf(aux))) {
-                                current_statement_begin__ = 951;
-                                stan::math::assign(gamma_temp, get_base1(eta,n,"eta",1));
-                            } else {
-                                current_statement_begin__ = 952;
-                                stan::math::assign(gamma_temp, gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__));
-                            }
-                            current_statement_begin__ = 953;
-                            if (as_bool(logical_lt(gamma_temp,poisson_max))) {
-                                current_statement_begin__ = 954;
-                                stan::math::assign(mean_PPD, (mean_PPD + poisson_rng(gamma_temp, base_rng__)));
-                            } else {
-                                current_statement_begin__ = 955;
-                                stan::math::assign(mean_PPD, (mean_PPD + normal_rng(gamma_temp,sqrt(gamma_temp), base_rng__)));
-                            }
-                        }
-                    }
-                } else if (as_bool(logical_eq(family,4))) {
-
-                    current_statement_begin__ = 959;
-                    stan::math::assign(eta, linkinv_binom(eta,link, pstream__));
-                    current_statement_begin__ = 960;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 960;
-                        stan::math::assign(mean_PPD, (mean_PPD + binomial_rng(get_base1(trials,n,"trials",1),inv_logit(get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                } else if (as_bool(logical_eq(family,5))) {
-
-                    current_statement_begin__ = 963;
-                    if (as_bool(logical_gt(link,1))) {
-                        current_statement_begin__ = 963;
-                        stan::math::assign(eta, linkinv_gamma(eta,link, pstream__));
-                    }
-                    current_statement_begin__ = 964;
-                    for (int n = 1; n <= N; ++n) {
-                        current_statement_begin__ = 964;
-                        stan::math::assign(mean_PPD, (mean_PPD + gamma_rng(aux,(aux / get_base1(eta,n,"eta",1)), base_rng__)));
-                    }
-                }
-            }
-            current_statement_begin__ = 967;
-            stan::math::assign(mean_PPD, (mean_PPD / N));
-        } catch (const std::exception& e) {
-            stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
-            // Next line prevents compiler griping about no return
-            throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
-        }
-
-        // validate generated quantities
-
-        // write generated quantities
-        vars__.push_back(mean_PPD);
-        for (int k_0__ = 0; k_0__ < has_intercept; ++k_0__) {
-            vars__.push_back(alpha[k_0__]);
-        }
-
-    }
-
-    template <typename RNG>
-    void write_array(RNG& base_rng,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
-                     Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
-                     bool include_tparams = true,
-                     bool include_gqs = true,
-                     std::ostream* pstream = 0) const {
-      std::vector<double> params_r_vec(params_r.size());
-      for (int i = 0; i < params_r.size(); ++i)
-        params_r_vec[i] = params_r(i);
-      std::vector<double> vars_vec;
-      std::vector<int> params_i_vec;
-      write_array(base_rng,params_r_vec,params_i_vec,vars_vec,include_tparams,include_gqs,pstream);
-      vars.resize(vars_vec.size());
-      for (int i = 0; i < vars.size(); ++i)
-        vars(i) = vars_vec[i];
-    }
-
-    static std::string model_name() {
-        return "model_spatial";
-    }
-
-
-    void constrained_param_names(std::vector<std::string>& param_names__,
-                                 bool include_tparams__ = true,
-                                 bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (N - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_neq(model_type,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "tau";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= has_aux; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "psi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-
-    void unconstrained_param_names(std::vector<std::string>& param_names__,
-                                   bool include_tparams__ = true,
-                                   bool include_gqs__ = true) const {
-        std::stringstream param_name_stream__;
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "gamma" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "z_beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (logical_eq(model_type,1) ? 0 : N ); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "theta_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= (N - 1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi_raw" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_neq(model_type,1); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "rho" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "tau";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "global" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= hs; ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "local" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_1__ = 1; k_1__ <= K; ++k_1__) {
-            for (int k_0__ = 1; k_0__ <= (primitive_value(logical_eq(prior_dist,5)) || primitive_value(logical_eq(prior_dist,6))); ++k_0__) {
-                param_name_stream__.str(std::string());
-                param_name_stream__ << "mix" << '.' << k_0__ << '.' << k_1__;
-                param_names__.push_back(param_name_stream__.str());
-            }
-        }
-        for (int k_0__ = 1; k_0__ <= has_aux; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "aux_unscaled" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= logical_eq(prior_dist,6); ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "one_over_lambda" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__ && !include_tparams__) return;
-        for (int k_0__ = 1; k_0__ <= K; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "beta" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "aux";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "phi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-        for (int k_0__ = 1; k_0__ <= N; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "psi" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-
-        if (!include_gqs__) return;
-        param_name_stream__.str(std::string());
-        param_name_stream__ << "mean_PPD";
-        param_names__.push_back(param_name_stream__.str());
-        for (int k_0__ = 1; k_0__ <= has_intercept; ++k_0__) {
-            param_name_stream__.str(std::string());
-            param_name_stream__ << "alpha" << '.' << k_0__;
-            param_names__.push_back(param_name_stream__.str());
-        }
-    }
-
-}; // model
-
-}
-
-
-
-
-#endif

From 6efcfe41b648a2e7a90b5e2f110798ade8b6bebd Mon Sep 17 00:00:00 2001
From: Ben Goodrich <goodrich.ben@gmail.com>
Date: Sun, 28 Oct 2018 14:10:47 -0400
Subject: [PATCH 55/59] rm src/Modules*

---
 src/Modules.cpp | 203 ------------------------------------------------
 src/Modules.o   | Bin 87697660 -> 0 bytes
 2 files changed, 203 deletions(-)
 delete mode 100644 src/Modules.cpp
 delete mode 100644 src/Modules.o

diff --git a/src/Modules.cpp b/src/Modules.cpp
deleted file mode 100644
index cbc7d3b48..000000000
--- a/src/Modules.cpp
+++ /dev/null
@@ -1,203 +0,0 @@
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4bernoulli_mod) {
-
-
-    class_<rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> >("model_bernoulli")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_bernoulli_namespace::model_bernoulli, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4binomial_mod) {
-
-
-    class_<rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> >("model_binomial")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_binomial_namespace::model_binomial, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4continuous_mod) {
-
-
-    class_<rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> >("model_continuous")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_continuous_namespace::model_continuous, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4count_mod) {
-
-
-    class_<rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> >("model_count")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_count_namespace::model_count, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4lm_mod) {
-
-
-    class_<rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> >("model_lm")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_lm_namespace::model_lm, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4polr_mod) {
-
-
-    class_<rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> >("model_polr")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_polr_namespace::model_polr, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
-#include <Rcpp.h>
-using namespace Rcpp ;
-#include "include/models.hpp"
-
-RCPP_MODULE(stan_fit4spatial_mod) {
-
-
-    class_<rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> >("model_spatial")
-
-    .constructor<SEXP,SEXP>()
-
-
-    .method("call_sampler", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::call_sampler)
-    .method("param_names", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_names)
-    .method("param_names_oi", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_names_oi)
-    .method("param_fnames_oi", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_fnames_oi)
-    .method("param_dims", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_dims)
-    .method("param_dims_oi", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_dims_oi)
-    .method("update_param_oi", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::update_param_oi)
-    .method("param_oi_tidx", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::param_oi_tidx)
-    .method("grad_log_prob", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::grad_log_prob)
-    .method("log_prob", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::log_prob)
-    .method("unconstrain_pars", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::unconstrain_pars)
-    .method("constrain_pars", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::constrain_pars)
-    .method("num_pars_unconstrained", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::num_pars_unconstrained)
-    .method("unconstrained_param_names", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::unconstrained_param_names)
-    .method("constrained_param_names", &rstan::stan_fit<model_spatial_namespace::model_spatial, boost::random::ecuyer1988> ::constrained_param_names)
-    ;
-}
diff --git a/src/Modules.o b/src/Modules.o
deleted file mode 100644
index 2d9ff9f9ccc32e699b65793f20d8ae4dfb98c33e..0000000000000000000000000000000000000000
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literal 0
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From 9eddcc2e0ca9819d8422c6f505a13eff5917ad40 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 28 Oct 2018 14:15:58 -0400
Subject: [PATCH 56/59] dropping INLA requirement

---
 R/stan_besag.R | 2 --
 R/stan_bym.R   | 2 --
 R/stan_bym2.R  | 2 --
 3 files changed, 6 deletions(-)

diff --git a/R/stan_besag.R b/R/stan_besag.R
index 29db31bf0..1219ba337 100644
--- a/R/stan_besag.R
+++ b/R/stan_besag.R
@@ -116,8 +116,6 @@ stan_besag <- function(formula,
                      adapt_delta = NULL,
                      QR = FALSE) {
   stan_function <- "stan_besag"
-  if (!requireNamespace("INLA", quietly = TRUE))
-    stop(paste("Please install and load the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)
diff --git a/R/stan_bym.R b/R/stan_bym.R
index ad8bcb5a7..475de20ed 100644
--- a/R/stan_bym.R
+++ b/R/stan_bym.R
@@ -121,8 +121,6 @@ stan_bym <- function(formula,
                       adapt_delta = NULL,
                       QR = FALSE) {
   stan_function <- "stan_bym"
-  if (!requireNamespace("INLA", quietly = TRUE))
-    stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)
diff --git a/R/stan_bym2.R b/R/stan_bym2.R
index c9e45ae91..a1d2933ad 100644
--- a/R/stan_bym2.R
+++ b/R/stan_bym2.R
@@ -129,8 +129,6 @@ stan_bym2 <- function(formula,
                         adapt_delta = NULL,
                         QR = FALSE) {
   stan_function <- "stan_bym2"
-  if (!requireNamespace("INLA", quietly = TRUE))
-    stop(paste("Please install the INLA package before using", stan_function))
   mc <- match.call(expand.dots = FALSE)
   algorithm <- match.arg(algorithm)
   family <- validate_family(family)

From e0dfbcc9fe84ccfe65f544c710f589f64ad08741 Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 28 Oct 2018 14:52:49 -0400
Subject: [PATCH 57/59] fix family/link bounds in spatial.stan

---
 R/stan_spatial.fit.R        | 2 +-
 src/stan_files/spatial.stan | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/stan_spatial.fit.R b/R/stan_spatial.fit.R
index 060848476..55d4a2a85 100644
--- a/R/stan_spatial.fit.R
+++ b/R/stan_spatial.fit.R
@@ -38,7 +38,7 @@ stan_spatial.fit <- function(x, y, w,
                              QR = FALSE) {
 
   w[upper.tri(w)] <- 0
-  
+
   # convert W to a sparse matrix if not already sparse.
   if(!is(w, "sparseMatrix"))
     w <- Matrix(w, sparse = TRUE)
diff --git a/src/stan_files/spatial.stan b/src/stan_files/spatial.stan
index 384e863a2..840e003f1 100644
--- a/src/stan_files/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -11,8 +11,8 @@ data {
   int<lower=0> N;                 // number of regions
   int<lower=0> K;                 // number of predictors (inc intercept)
   matrix[N,K] X;                  // model matrix
-  int<lower=1,upper=8> family;    // 1 gaussian, 2 poisson, 3 neg_binomial, 4 binomial, 5 gamma
-  int link;
+  int<lower=1,upper=7> family;    // 1 gaussian; 2 gamma; 5 binomial; 6 poisson; 7 neg_binomial_2
+  int<lower=1,upper=5> link;
   int<lower=0,upper=1> is_continuous;
   int<lower=0,upper=1> has_aux;
   int<lower=1,upper=3> model_type;       // Besag = 1; BYM = 2; BYM2 = 3

From 5b089362460fde4a9b673b6f8f4bbee33bcc4caf Mon Sep 17 00:00:00 2001
From: Imad Ali <imadmali@gmail.com>
Date: Sun, 28 Oct 2018 15:18:44 -0400
Subject: [PATCH 58/59] refactoring in-line declarations and likelihood
 contributions

---
 src/stan_files/spatial.stan | 20 +++++++-------------
 vignettes/spatial.Rmd       |  3 +--
 2 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/src/stan_files/spatial.stan b/src/stan_files/spatial.stan
index 840e003f1..24e9c8d8f 100644
--- a/src/stan_files/spatial.stan
+++ b/src/stan_files/spatial.stan
@@ -161,20 +161,16 @@ model {
     eta = X * beta + psi;
   // likelihoods
   if (family == 1) {
-    eta = linkinv_gauss(eta, link);
-    target+= normal_lpdf(y_real | eta, aux);
+    target+= normal_lpdf(y_real | linkinv_gauss(eta, link), aux);
   }
   else if (family == 6) {
-    eta = linkinv_count(eta, link);
-    target+= poisson_lpmf(y_int | eta);
+    target+= poisson_lpmf(y_int | linkinv_count(eta, link));
   }
   else if (family == 7) {
-    eta = linkinv_count(eta, link);
-    target += neg_binomial_2_lpmf(y_int | eta, aux);
+    target += neg_binomial_2_lpmf(y_int | linkinv_count(eta, link), aux);
   }
   else if (family == 5) {
-    eta = linkinv_binom(eta, link);
-    target+= binomial_lpmf(y_int | trials, eta);
+    target+= binomial_lpmf(y_int | trials, linkinv_binom(eta, link));
   }
   else if (family == 2) {
     target += GammaReg(y_real, eta, aux, link, sum_log_y);
@@ -276,9 +272,7 @@ generated quantities {
   real mean_PPD = 0;
   real alpha[has_intercept];
   {
-    vector[N] lp_tf;
-    vector[N] eta;
-    eta = X * beta + psi;
+    vector[N] eta = X * beta + psi;
     if (has_intercept == 1) {
       alpha[1] = gamma[1] - dot_product(beta, xbar);
       if ((family == 5 && link == 4)) { // binomial
@@ -319,7 +313,7 @@ generated quantities {
       }
     }
     else if (family == 5) {
-      lp_tf = linkinv_binom(eta, link);
+      vector[N] lp_tf = linkinv_binom(eta, link);
       for (n in 1:N) mean_PPD += binomial_rng(trials[n], lp_tf[n]);
     }
     else if (family == 2) {
@@ -327,5 +321,5 @@ generated quantities {
       for (n in 1:N) mean_PPD += gamma_rng(aux, aux / eta[n]);
     }
   }
-  mean_PPD = mean_PPD / N;
+  mean_PPD /= N;
 }
diff --git a/vignettes/spatial.Rmd b/vignettes/spatial.Rmd
index f2734e293..231afb5b3 100644
--- a/vignettes/spatial.Rmd
+++ b/vignettes/spatial.Rmd
@@ -30,8 +30,7 @@ This vignette explains how to account for spatial variation with conditional aut
 ```
 
 Steps 3 and 4 are covered in more depth by the vignette entitled ["How to Use
-the __rstanarm__ Package"](rstanarm.html). This vignette focuses on Step 1 when
-the likelihood is the product of beta distributions.
+the __rstanarm__ Package"](rstanarm.html).
 
 ## Likelihood
 

From 8eb3c7f43a52860949ec3a00e8e5a05323ef5c5b Mon Sep 17 00:00:00 2001
From: Ben Goodrich <goodrich.ben@gmail.com>
Date: Sun, 28 Oct 2018 17:13:04 -0400
Subject: [PATCH 59/59] NAMESPACE: update

---
 NAMESPACE | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 8f1042f8f..ac3e621ae 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -29,6 +29,7 @@ S3method(launch_shinystan,stanreg)
 S3method(log_lik,stanjm)
 S3method(log_lik,stanmvreg)
 S3method(log_lik,stanreg)
+S3method(loo,stanreg)
 S3method(loo_linpred,stanreg)
 S3method(loo_model_weights,stanreg_list)
 S3method(loo_predict,stanreg)
@@ -81,6 +82,7 @@ S3method(update,stanmvreg)
 S3method(update,stanreg)
 S3method(vcov,stanreg)
 S3method(waic,stanreg)
+export()
 export(R2)
 export(Surv)
 export(VarCorr)
@@ -104,7 +106,6 @@ export(launch_shinystan)
 export(lkj)
 export(log_lik)
 export(loo)
-export(loo.stanreg)
 export(loo_linpred)
 export(loo_model_weights)
 export(loo_predict)
@@ -139,8 +140,6 @@ export(stan_betareg)
 export(stan_betareg.fit)
 export(stan_biglm)
 export(stan_biglm.fit)
-export(stan_bym)
-export(stan_bym2)
 export(stan_clogit)
 export(stan_gamm4)
 export(stan_glm)