Move shinystan to suggests and bump Roxygen

.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

DESCRIPTION

@@ -44,7 +44,6 @@ Imports:
     posterior, 
     rstan (>= 2.32.0),
     rstantools (>= 2.1.0),
-    shinystan (>= 2.3.0),
     stats,
     survival (>= 2.40.1),
     RcppParallel (>= 5.0.1),
@@ -62,6 +61,7 @@ Suggests:
     mgcv (>= 1.8-13),
     rmarkdown,
     roxygen2,
+    shinystan (>= 2.3.0),
     StanHeaders (>= 2.21.0),
     testthat (>= 1.0.2),
     gamm4,
@@ -76,4 +76,4 @@ UseLTO: true
 NeedsCompilation: yes
 URL: https://mc-stan.org/rstanarm/, https://discourse.mc-stan.org
 BugReports: https://github.com/stan-dev/rstanarm/issues
-RoxygenNote: 7.3.3
+Config/roxygen2/version: 8.0.0

NAMESPACE

@@ -33,7 +33,6 @@ S3method(get_y,stanmvreg)
 S3method(get_z,lmerMod)
 S3method(get_z,stanmvreg)
 S3method(kfold,stanreg)
-S3method(launch_shinystan,stanreg)
 S3method(log_lik,stanjm)
 S3method(log_lik,stanmvreg)
 S3method(log_lik,stanreg)
@@ -87,6 +86,7 @@ S3method(residuals,stanmvreg)
 S3method(residuals,stanreg)
 S3method(se,stanmvreg)
 S3method(se,stanreg)
+S3method(shinystan::launch_shinystan,stanreg)
 S3method(sigma,stanmvreg)
 S3method(sigma,stanreg)
 S3method(summary,stanmvreg)
@@ -195,7 +195,6 @@ import(Rcpp)
 import(bayesplot)
 import(methods)
 import(rstantools)
-import(shinystan)
 import(stats)
 importFrom(Matrix,Matrix)
 importFrom(Matrix,t)

R/doc-rstanarm-package.R

@@ -20,14 +20,12 @@
 #' @useDynLib rstanarm, .registration = TRUE
 #'
 #' @import methods
-#' @importFrom rstan optimizing sampling vb constrain_pars extract
-#'   extract_sparse_parts get_posterior_mean stanc
+#' @importFrom rstan optimizing sampling vb constrain_pars extract extract_sparse_parts get_posterior_mean stanc
 #' @importFrom utils capture.output
 #' @importFrom RcppParallel RcppParallelLibs
 #' @import stats
 #' @import Rcpp
 #' @import bayesplot
-#' @import shinystan
 #' @import rstantools
 #' @export log_lik posterior_linpred posterior_epred posterior_predict posterior_interval
 #' @export predictive_interval predictive_error prior_summary bayes_R2

R/launch_shinystan.R

@@ -1,68 +1,68 @@
 # 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.
 
 #' Using the ShinyStan GUI with rstanarm models
-#' 
+#'
 #' The ShinyStan interface provides visual and numerical summaries of model
 #' parameters and convergence diagnostics.
-#' 
+#'
 #' @aliases launch_shinystan
 #' @export
-#' 
+#'
 #' @templateVar stanregArg object
 #' @template args-stanreg-object
 #' @inheritParams shinystan::launch_shinystan
-#' @param ppd Should \pkg{rstanarm} draw from the posterior predictive 
-#'   distribution before launching ShinyStan? The default is \code{TRUE}, 
-#'   although for very large objects it can be convenient to set it to 
-#'   \code{FALSE} as drawing from the posterior predictive distribution can be 
-#'   time consuming. If \code{ppd} is \code{TRUE} then graphical posterior 
+#' @param ppd Should \pkg{rstanarm} draw from the posterior predictive
+#'   distribution before launching ShinyStan? The default is \code{TRUE},
+#'   although for very large objects it can be convenient to set it to
+#'   \code{FALSE} as drawing from the posterior predictive distribution can be
+#'   time consuming. If \code{ppd} is \code{TRUE} then graphical posterior
 #'   predictive checks are available when ShinyStan is launched.
-#' @param seed Passed to \link[=pp_check]{pp_check} if 
+#' @param seed Passed to \link[=pp_check]{pp_check} if
 #'   \code{ppd} is \code{TRUE}.
 #' @param model_name,note Optional arguments passed to
 #'   \code{\link[shinystan]{as.shinystan}}.
-#'   
+#'
 #' @details The \code{\link[shinystan]{launch_shinystan}} function will accept a
-#'   \code{\link[=stanreg-objects]{stanreg}} object as input. Currently, almost 
-#'   any model fit using one of \pkg{rstanarm}'s model-fitting functions can be 
-#'   used with ShinyStan. The only exception is that ShinyStan does not 
-#'   currently support \pkg{rstanarm} models fit using 
-#'   \code{algorithm='optimizing'}. See the 
-#'   \pkg{\link[shinystan:shinystan-package]{shinystan}} package documentation for more 
+#'   \code{\link[=stanreg-objects]{stanreg}} object as input. Currently, almost
+#'   any model fit using one of \pkg{rstanarm}'s model-fitting functions can be
+#'   used with ShinyStan. The only exception is that ShinyStan does not
+#'   currently support \pkg{rstanarm} models fit using
+#'   \code{algorithm='optimizing'}. See the
+#'   \pkg{\link[shinystan:shinystan-package]{shinystan}} package documentation for more
 #'   information.
-#'   
+#'
 #' @section Faster launch times:
 #' For some \pkg{rstanarm} models ShinyStan may take a very long time to launch.
 #' If this is the case with one of your models you may be able to speed up
 #' \code{launch_shinystan} in one of several ways:
 #' \describe{
 #'   \item{Prevent ShinyStan from preparing graphical posterior predictive
 #'   checks:}{
-#'   When used with a \code{\link[=stanreg-objects]{stanreg}} object 
-#'   (\pkg{rstanarm} model object) ShinyStan will draw from the posterior 
-#'   predictive distribution and prepare graphical posterior predictive checks 
-#'   before launching. That way when you go to the PPcheck page the plots are 
+#'   When used with a \code{\link[=stanreg-objects]{stanreg}} object
+#'   (\pkg{rstanarm} model object) ShinyStan will draw from the posterior
+#'   predictive distribution and prepare graphical posterior predictive checks
+#'   before launching. That way when you go to the PPcheck page the plots are
 #'   immediately available. This can be time consuming for models fit to very
 #'   large datasets and you can prevent this behavior by creating a shinystan
-#'   object before calling \code{launch_shinystan}. To do this use 
+#'   object before calling \code{launch_shinystan}. To do this use
 #'   \code{\link[shinystan]{as.shinystan}} with optional argument \code{ppd} set
 #'   to \code{FALSE} (see the Examples section below). When you then launch
-#'   ShinyStan and go to the PPcheck page the plots will no longer be 
+#'   ShinyStan and go to the PPcheck page the plots will no longer be
 #'   automatically generated and you will be presented with the standard
 #'   interface requiring you to first specify the appropriate \eqn{y} and
 #'   \eqn{yrep}, which can be done for many but not all \pkg{rstanarm} models.
@@ -73,46 +73,61 @@
 #'   \code{\link[shinystan]{as.shinystan}} can reduce \emph{future} launch
 #'   times. That is, \code{launch_shinystan(sso)} will be faster than
 #'   \code{launch_shinystan(fit)}, where \code{sso} is a shinystan object and
-#'   \code{fit} is a stanreg object. It still may take some time for 
+#'   \code{fit} is a stanreg object. It still may take some time for
 #'   \code{as.shinystan} to create \code{sso} initially, but each time you
 #'   subsequently call \code{launch_shinystan(sso)} it will reuse \code{sso}
 #'   instead of internally creating a shinystan object every time. See the
 #'   Examples section below.}
 #' }
-#' 
+#'
 #' @template reference-bayesvis
 #' @template reference-muth
-#'   
+#'
 #' @examples
 #' if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
 #' \dontrun{
-#' if (!exists("example_model")) example(example_model) 
-#' 
+#' if (!exists("example_model")) example(example_model)
+#'
 #' # Launch the ShinyStan app without saving the resulting shinystan object
 #' if (interactive()) launch_shinystan(example_model)
-#' 
+#'
 #' # Launch the ShinyStan app (saving resulting shinystan object as sso)
 #' if (interactive()) sso <- launch_shinystan(example_model)
-#' 
+#'
 #' # First create shinystan object then call launch_shinystan
 #' sso <- shinystan::as.shinystan(example_model)
 #' if (interactive()) launch_shinystan(sso)
-#' 
+#'
 #' # Prevent ShinyStan from preparing graphical posterior predictive checks that
 #' # can be time consuming. example_model is small enough that it won't matter
 #' # much here but in general this can help speed up launch_shinystan
 #' sso <- shinystan::as.shinystan(example_model, ppd = FALSE)
 #' if (interactive()) launch_shinystan(sso)
 #' }
 #' }
+launch_shinystan <- function(object, ...) {
+  if (!requireNamespace("shinystan", quietly = TRUE)) {
+    stop(
+      "Package 'shinystan' is not installed but is required in order to use `launch_shinystan()`.",
+      call. = FALSE
+    )
+  }
+
+  shinystan::launch_shinystan(object, ...)
+}
+
+#' @rdname launch_shinystan
+#' @exportS3Method shinystan::launch_shinystan
 launch_shinystan.stanreg <-
-  function(object,
-           ppd = TRUE, 
-           seed = 1234, 
-           model_name = NULL, 
-           note = NULL, 
-           rstudio = getOption("shinystan.rstudio"), 
-           ...) {
+  function(
+    object,
+    ppd = TRUE,
+    seed = 1234,
+    model_name = NULL,
+    note = NULL,
+    rstudio = getOption("shinystan.rstudio"),
+    ...
+  ) {
     sso <-
       shinystan::as.shinystan(
         object,
@@ -121,5 +136,6 @@ launch_shinystan.stanreg <-
         model_name = model_name,
         note = note
       )
+
     shinystan::launch_shinystan(sso, rstudio = rstudio, ...)
   }

R/posterior_survfit.R

@@ -531,8 +531,7 @@ posterior_survfit <- function(object, newdataLong = NULL, newdataEvent = NULL,
 #'
 #' @method plot survfit.stanjm
 #' @export
-#' @importFrom ggplot2 ggplot aes_string geom_line geom_ribbon
-#'   facet_wrap labs coord_cartesian
+#' @importFrom ggplot2 ggplot aes_string geom_line geom_ribbon facet_wrap labs coord_cartesian
 #'
 #' @templateVar idsArg ids
 #' @templateVar labsArg xlab,ylab

R/posterior_traj.R

@@ -463,8 +463,7 @@ posterior_traj <- function(object, m = 1, newdata = NULL, newdataLong = NULL,
 #' 
 #' @method plot predict.stanjm
 #' @export
-#' @importFrom ggplot2 ggplot aes aes_string geom_line geom_smooth geom_ribbon 
-#'   geom_point facet_wrap geom_vline labs ggplot_build theme_bw
+#' @importFrom ggplot2 ggplot aes aes_string geom_line geom_smooth geom_ribbon geom_point facet_wrap geom_vline labs ggplot_build theme_bw
 #'    
 #' @templateVar labsArg xlab,ylab
 #' @templateVar scalesArg facet_scales

R/posterior_vs_prior.R

@@ -91,8 +91,7 @@
 #' gg_polr + ggplot2::coord_flip()
 #' }
 #' }
-#' @importFrom ggplot2 geom_pointrange facet_wrap aes_string labs
-#'   scale_x_discrete element_line element_text
+#' @importFrom ggplot2 geom_pointrange facet_wrap aes_string labs scale_x_discrete element_line element_text
 #' 
 posterior_vs_prior <- function(object, ...) {
   UseMethod("posterior_vs_prior")