prepare for release of 2.13.1

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: rstanarm
 Type: Package
 Title: Bayesian Applied Regression Modeling via Stan
-Version: 2.12.2
-Date: 2016-09-17
+Version: 2.13.1
+Date: 2016-11-20
 Authors@R: c(person("Jonah", "Gabry", email = "[email protected]", role = "aut"),
              person("Trustees of", "Columbia University", role = "cph"),
              person("R Core", "Deveopment Team", role = "cph", 

R/plots.R

@@ -60,6 +60,7 @@
 #'   \code{\link{pp_check}} for graphical posterior predictive checks.
 #'
 #' @examples
+#' \donttest{
 #' # Use rstanarm example model
 #' if (!exists("example_model")) example(example_model)
 #' fit <- example_model
@@ -78,12 +79,10 @@
 #' plot(fit, "areas", regex_pars = "period",
 #'      prob = 0.5, prob_outer = 0.9)
 #' 
-#' \donttest{
 #' # Make the same plot by extracting posterior draws and calling
 #' # bayesplot::mcmc_areas directly
 #' x <- as.array(fit, regex_pars = "period")
 #' bayesplot::mcmc_areas(x, prob = 0.5, prob_outer = 0.9)
-#' }
 #'
 #'
 #' ##################################
@@ -139,11 +138,9 @@
 #' # changing facet layout 
 #' plot(fit, "trace", pars = c("(Intercept)", "period2"),
 #'      facet_args = list(nrow = 2))
-#' \donttest{
 #' # same plot by calling bayesplot::mcmc_trace directly
 #' x <- as.array(fit, pars = c("(Intercept)", "period2"))
 #' bayesplot::mcmc_trace(x, facet_args = list(nrow = 2))
-#' }
 #'
 #'
 #' ############
@@ -155,7 +152,7 @@
 #' plot(fit, regex_pars = "herd:[279]")
 #' plot(fit, regex_pars = "herd:[279]|period2")
 #' plot(fit, regex_pars = c("herd:[279]", "period2"))
-#'
+#' }
 #'
 #' # For graphical posterior predictive checks see
 #' # help("pp_check.stanreg")

inst/NEWS

@@ -1,3 +1,17 @@
+Sun November 20 2016 (2.13.1)
+1. Bug fixes
+  * Fix bug in reloo() if data was not specified
+  * Fix bug in pp_validate() that was only introduced on GitHub
+2. New features
+  * Uses the new bayesplot and rstantools R packages
+  * The new prior_summary() function can be used to figure out what priors were actually used
+  * stan_gamm4() is better implemented, can be followed by plot_nonlinear(), 
+    posterior_predict (with newdata), etc.
+  * Hyperparameters (i.e. covariance matrices in general) for lme4 style models are now
+    returned by as.matrix()
+  * pp_validate() can now be used if optimization or variational Bayesian inference was
+    used to estimate the original model
+
 Fri September 9 2016 (2.12.1)
 1. Bug fixes
   * Fix for bad bug in posterior_predict() when factor labels have spaces in lme4-style models