Cran fixes

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.0.0
-Date: 2024-03-12 23:48:24 UTC
-SHA: 9feb9f5f1965fd7b48031b941d6570bde33f538b
+Version: 1.0.3
+Date: 2024-07-18 19:00:17 UTC
+SHA: 4d50ded599f4b61cd0d1e784f008aef30ec6462b

DESCRIPTION

@@ -1,16 +1,14 @@
 Package: serocalculator
 Type: Package
 Title: Estimating Infection Rates from Serological Data
-Version: 1.2.0.9000
+Version: 1.0.3
 Authors@R: c(
   person(given = "Peter", family = "Teunis", email = "[email protected]", role = c("aut", "cph"), comment = "Author of the method and original code."),
   person(given = "Kristina", family = "Lai", email = "[email protected]", role = c("aut", "cre")), 
   person(given = "Chris", family = "Orwa", role = "aut"), 
   person(given = "Kristen", family = "Aiemjoy", email = "[email protected]", role = c("aut")),
   person(given = "Douglas Ezra", family = "Morrison", email = "[email protected]", role = c("aut")))
-Description: Translates antibody levels measured in cross-sectional population 
-  samples into estimates of the frequency with which seroconversions (infections) 
-  occur in the sampled populations. Replaces the previous `seroincidence` package.
+Description: Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous 'seroincidence' package. Methods originally published in Simonsen et al. (2009) <doi:10.1002/sim.3592> and Teunis et al. (2012) <doi:10.1002/sim.5322>, and further developed in subsequent publications by de Graaf et al. (2014) <doi:10.1016/j.epidem.2014.08.002>, Teunis et al. (2016) <doi:10.1016/j.epidem.2016.04.001>, and Teunis et al. (2020) <doi:10.1002/sim.8578>.
 Depends: R (>= 4.1.0)
 License: GPL-3
 Imports: 

NAMESPACE

@@ -33,17 +33,15 @@ export(check_pop_data)
 export(est.incidence)
 export(est.incidence.by)
 export(f_dev)
-export(f_dev0)
 export(fdev)
-export(getAdditionalData)
-export(get_additional_data)
 export(graph.curve.params)
 export(graph.loglik)
 export(llik)
 export(load_curve_params)
 export(load_noise_params)
 export(load_pop_data)
 export(log_likelihood)
+export(plot_curve_params_one_ab)
 export(sim.cs)
 export(sim.cs.multi)
 importFrom(Rcpp,sourceCpp)

NEWS.md

@@ -1,6 +1,16 @@
-# serocalculator (development version)
 
-# serocalculator 1.2.0
+# serocalculator (published versions)
+
+## serocalculator 1.0.3
+* Published to CRAN
+
+## serocalculator 1.0.2
+* Updated documentation and functionality based on CRAN resubmission
+* Deleted get_additional_data.R as it is no longer used
+
+# serocalculator (development versions)
+
+## serocalculator 1.2.0
 * Added `test-summary.pop_data` test
 
 * Modified `test-est.incidence` test
@@ -10,7 +20,7 @@
 * Added `verbose` option for `check_pop_data()`, changing default behavior
 to avoid printing an OK message.
 
-# serocalculator 1.1.0
+## serocalculator 1.1.0
 
 * Renamed `llik()` to `log_likelihood()`
 
@@ -46,10 +56,11 @@ to avoid printing an OK message.
 
 * Replaced column name comparison on `check_pop_data()` to use attribute name on `pop_data` class
 
-# serocalculator 1.0.1
+## serocalculator 1.0.1
 
 * added `n_points` argument to `plot_curve_params_one_ab()`
 * Added `type = "age-scatter"` option for `autoplot.pop_data()`
+* Updated documentation for sim.cs and log-likelihood
 
 ## serocalculator 1.0.0
 

R/autoplot.curve_params.R

@@ -10,19 +10,21 @@
 #' @return a [ggplot2::ggplot()] object
 #' @export
 #' @examples
+#' \donttest{
 #' library(dplyr)
 #' library(ggplot2)
+#' library(magrittr)
 #'
 #' curve = load_curve_params("https://osf.io/download/rtw5k/") %>%
 #'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
 #'   slice(1:100, .by = antigen_iso)  %>% # Reduce dataset for the purposes of this example
 #'   autoplot()
 #'
 #' curve
-#'
+#'}
 autoplot.curve_params = function(
     object,
-    antigen_isos = object$antigen_iso %>% unique(),
+    antigen_isos = unique(object$antigen_iso),
     ncol = min(3, length(antigen_isos)),
     ...) {
   split_data <- object %>%

R/autoplot.pop_data.R

@@ -11,6 +11,7 @@
 #' @return a [ggplot2::ggplot] object
 #'
 #' @examples
+#' \donttest{
 #' library(dplyr)
 #' library(ggplot2)
 #'
@@ -24,6 +25,7 @@
 #'
 #' xs_data %>% autoplot(strata = "Country", type = "density")
 #' xs_data %>% autoplot(strata = "Country", type = "age-scatter")
+#' }
 #' @export
 autoplot.pop_data <- function(
     object,

R/autoplot.seroincidence.R

@@ -7,7 +7,7 @@
 #' @return a [ggplot2::ggplot()]
 #' @export
 #' @examples
-#'
+#'\donttest{
 #' library(dplyr)
 #' library(ggplot2)
 #'
@@ -29,7 +29,7 @@
 #'
 #' # Plot the log-likelihood curve
 #' autoplot(est1)
-#'
+#' }
 autoplot.seroincidence =
   function(object, log_x = FALSE, ...)
 {

R/autoplot.seroincidence.by.R

@@ -10,7 +10,7 @@
 #'
 #' library(dplyr)
 #' library(ggplot2)
-#'
+#'\donttest{
 #' xs_data <- "https://osf.io/download//n6cp3/" %>%
 #'   load_pop_data()
 #'
@@ -20,6 +20,7 @@
 #'
 #' noise <- load_noise_params("https://osf.io/download//hqy4v/")
 #'
+#'
 #' est2 <- est.incidence.by(
 #'   strata = c("catchment"),
 #'   pop_data = xs_data %>% filter(Country == "Pakistan"),
@@ -32,7 +33,7 @@
 #'
 #' # Plot the log-likelihood curve
 #' autoplot(est2)
-#'
+#' }
 autoplot.seroincidence.by = function(
     object,
     ncol = min(3, length(object)),

R/autoplot.summary.seroincidence.by.R

@@ -10,10 +10,11 @@
 #' @return a [ggplot2::ggplot()] object
 #' @export
 #' @examples
-#'
+#'\donttest{
 #' library(dplyr)
 #' library(ggplot2)
 #'
+#'
 #' xs_data <- load_pop_data("https://osf.io/download//n6cp3/")
 #'
 #' curve <- load_curve_params("https://osf.io/download/rtw5k/") %>%
@@ -34,8 +35,8 @@
 #' est2sum <- summary(est2)
 #'
 #' autoplot(est2sum, "catchment")
-#'
-autoplot.summary.seroincidence.by <- function(
+#' }
+autoplot.summary.seroincidence.by = function(
     object,
     xvar,
     alpha = .7,

R/build_likelihood_function.R

@@ -10,7 +10,7 @@
     # add terms, e.g. for other antibodies
     for (cur_antigen in antigen_isos)
     {
-      res <- res +
+      res = res +
         f_dev(
           lambda = lambda,
           csdata = cross_sectional_data[[cur_antigen]],

R/df_to_array.R

@@ -23,19 +23,6 @@ df.to.array <- function(
 #' @param value_var_name a [character()] variable containing a variable name from `df` which contains the values for the intended array.
 #' @return an [array()] with dimensions defined by the variables in `df` listed in `dim_var_names`
 #'
-#' @examples
-#' library(dplyr)
-#' library(tidyr)
-#'
-#' df <- iris %>%
-#'   tidyr::pivot_longer(
-#'     names_to = "parameter",
-#'     cols = c("Sepal.Length", "Sepal.Width", "Petal.Width", "Petal.Length")
-#'   ) %>%
-#'   mutate(parameter = factor(parameter, levels = unique(parameter)))
-#' arr <- df %>% serocalculator:::df.to.array(dim_var_names = c("parameter", "Species"))
-#' ftable(arr[,,1:5])
-#' @noRd
 df_to_array <- function(
     df,
     dim_var_names,

R/est.incidence.R

@@ -14,7 +14,7 @@
 #' @returns a `"seroincidence"` object, which is a [stats::nlm()] fit object with extra meta-data attributes `lambda_start`, `antigen_isos`, and `ll_graph`
 #' @export
 #' @examples
-#'
+#'\donttest{
 #' library(dplyr)
 #'
 #' xs_data <- load_pop_data("https://osf.io/download//n6cp3/")
@@ -25,6 +25,7 @@
 #'
 #' noise <- load_noise_params("https://osf.io/download//hqy4v/")
 #'
+#'
 #' est1 <- est.incidence(
 #'   pop_data = xs_data %>% filter(Country == "Pakistan"),
 #'   curve_params = curves,
@@ -34,6 +35,7 @@
 #' )
 #'
 #' summary(est1)
+#' }
 est.incidence <- function(
     pop_data,
     curve_params,

R/est.incidence.by.R

@@ -32,7 +32,7 @@
 #'
 #' @export
 #' @examples
-#'
+#'\donttest{
 #' library(dplyr)
 #'
 #' xs_data <- load_pop_data("https://osf.io/download//n6cp3/")
@@ -43,6 +43,7 @@
 #'
 #' noise <- load_noise_params("https://osf.io/download//hqy4v/")
 #'
+#'
 #' est2 <- est.incidence.by(
 #'   strata = c("catchment"),
 #'   pop_data = xs_data %>% filter(Country == "Pakistan"),
@@ -54,7 +55,7 @@
 #' )
 #'
 #' summary(est2)
-#'
+#' }
 est.incidence.by <- function(
     pop_data,
     curve_params,

R/f_dev.r

@@ -76,43 +76,43 @@ fdev <- function(lambda, csdata, lnpars, cond)
 #'     cond = cur_noise_params
 #'   )
 f_dev0 <- function(
-      lambda,
-      csdata,
-      lnpars,
-      cond) {
-    res <- 0
-    lambda <- as.double(lambda)
-    y <- as.double(get_value(csdata))
-    a <- as.double(get_age(csdata))
-    nsubj <- as.integer(nrow(csdata))
-    y1 <- as.double(lnpars$y1)
-    alpha <- as.double(lnpars$alpha)
-    d <- as.double(lnpars$d)
-    nmc <- as.integer(length(y1))
-    step <- as.double(max(y1) / 100) # hack for numerical integrations
-    nu <- as.double(cond$nu)
-    eps <- as.double(cond$eps)
-    y.low <- as.double(cond$y.low)
-    y.high <- as.double(cond$y.high)
-    llpp <- .C(
-      "negloglik",
-      res = as.double(res),
-      lambda = lambda,
-      y = y,
-      a = a,
-      nsubj = nsubj,
-      nu = nu,
-      eps = eps,
-      step = step,
-      y.low = y.low,
-      y.high = y.high,
-      y1 = y1,
-      alpha = alpha,
-      d = d,
-      nmc = nmc
-    )
-    return(llpp$res)
-  }
+    lambda,
+    csdata,
+    lnpars,
+    cond) {
+  res <- 0
+  lambda <- as.double(lambda)
+  y <- as.double(get_value(csdata))
+  a <- as.double(get_age(csdata))
+  nsubj <- as.integer(nrow(csdata))
+  y1 <- as.double(lnpars$y1)
+  alpha <- as.double(lnpars$alpha)
+  d <- as.double(lnpars$d)
+  nmc <- as.integer(length(y1))
+  step <- as.double(max(y1) / 100) # hack for numerical integrations
+  nu <- as.double(cond$nu)
+  eps <- as.double(cond$eps)
+  y.low <- as.double(cond$y.low)
+  y.high <- as.double(cond$y.high)
+  llpp <- .C(
+    "negloglik",
+    res = as.double(res),
+    lambda = lambda,
+    y = y,
+    a = a,
+    nsubj = nsubj,
+    nu = nu,
+    eps = eps,
+    step = step,
+    y.low = y.low,
+    y.high = y.high,
+    y1 = y1,
+    alpha = alpha,
+    d = d,
+    nmc = nmc
+  )
+  return(llpp$res)
+}
 
 #' @title Calculate negative log-likelihood (deviance) for one antigen:isotype pair and several values of incidence
 #'