#688 move to WIP for now

DESCRIPTION

@@ -166,7 +166,6 @@ Suggests:
     poorman,
     posterior,
     PROreg,
-    projpred,
     pscl,
     psych,
     pvclust,

NAMESPACE

@@ -581,10 +581,8 @@ S3method(reshape_loadings,parameters_efa)
 S3method(se_satterthwaite,default)
 S3method(se_satterthwaite,gls)
 S3method(se_satterthwaite,lme)
-S3method(select_parameters,brmsfit)
 S3method(select_parameters,lm)
 S3method(select_parameters,merMod)
-S3method(select_parameters,stanreg)
 S3method(simulate_model,LORgee)
 S3method(simulate_model,MixMod)
 S3method(simulate_model,betamfx)

R/select_parameters.R

@@ -3,8 +3,7 @@
 #' This function performs an automated selection of the 'best' parameters,
 #' updating and returning the "best" model.
 #'
-#' @param model A statistical model (of class `lm`, `glm`,
-#'   `merMod`, `stanreg` or `brmsfit`).
+#' @param model A statistical model (of class `lm`, `glm`, or `merMod`).
 #' @param ... Arguments passed to or from other methods.
 #'
 #' @details
@@ -21,10 +20,6 @@
 #'     the cAIC can't be improved further.
 #'   }
 #'
-#'   \subsection{Bayesian models}{
-#'     For Bayesian models, it uses the **projpred** package.
-#'   }
-#'
 #' @examples
 #' model <- lm(mpg ~ ., data = mtcars)
 #' select_parameters(model)
@@ -42,25 +37,6 @@
 #' }
 #' }
 #'
-#' \dontrun{
-#' # rstanarm -------------------------------------------
-#' if (require("rstanarm") && require("projpred")) {
-#'   model <- stan_glm(
-#'     mpg ~ .,
-#'     data = mtcars,
-#'     iter = 500, refresh = 0, verbose = FALSE
-#'   )
-#'   select_parameters(model, cross_validation = TRUE)
-#'
-#'   model <- stan_glm(
-#'     mpg ~ cyl * disp * hp,
-#'     data = mtcars,
-#'     iter = 500, refresh = 0, verbose = FALSE
-#'   )
-#'   select_parameters(model, cross_validation = FALSE)
-#' }
-#' }
-#'
 #' @return The model refitted with optimal number of parameters.
 #' @export
 select_parameters <- function(model, ...) {

vignettes/parameters_selection.Rmd

@@ -135,13 +135,13 @@ and should not be used in place of **theoretical** or **hypothetical** reasons
 (*i.e.*, you should have *a priori* hypotheses about which parameters of your
 model you want to focus on).
 
-## Mixed and Bayesian models
+## Mixed models
 
 For simple linear regressions as above, the selection is made using the `step()`
 function (available in base R). This performs a
 [**stepwise**](https://en.wikipedia.org/wiki/Stepwise_regression) selection.
 However, this procedures is not available for other types of models, such as
-**mixed** or **Bayesian** models.
+**mixed** models.
 
 ### Mixed models