From 0fec72bc5d44852b306cc829d9d660294b716366 Mon Sep 17 00:00:00 2001
From: Jouni Helske <jouni.helske@utu.fi>
Date: Thu, 29 Aug 2024 13:09:16 +0300
Subject: [PATCH] to CRAN

---
 DESCRIPTION                | 4 ++--
 NAMESPACE                  | 1 +
 R/pp_check.R               | 6 +++---
 man/pp_check.walker_fit.Rd | 4 ++--
 vignettes/walker.Rmd       | 5 ++---
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 5dabe01..a392691 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -24,7 +24,8 @@ Suggests:
     testthat
 Depends:
     bayesplot,
-    R (>= 3.4.0)
+    R (>= 3.4.0),
+    rstan (>= 2.26.0)
 Imports:
     coda,
     dplyr,
@@ -36,7 +37,6 @@ Imports:
     Rcpp (>= 0.12.9),
     RcppParallel,
     rlang,
-    rstan (>= 2.26.0),
     rstantools (>= 2.0.0)
 LinkingTo:
     BH (>= 1.66.0),
diff --git a/NAMESPACE b/NAMESPACE
index 80f06d8..7b178d9 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -34,6 +34,7 @@ importFrom(RcppParallel,CxxFlags)
 importFrom(RcppParallel,RcppParallelLibs)
 importFrom(bayesplot,color_scheme_get)
 importFrom(bayesplot,pp_check)
+importFrom(bayesplot,ppc_ribbon)
 importFrom(bayesplot,theme_default)
 importFrom(coda,spectrum0.ar)
 importFrom(dplyr,group_by)
diff --git a/R/pp_check.R b/R/pp_check.R
index f2f24b7..e15246d 100644
--- a/R/pp_check.R
+++ b/R/pp_check.R
@@ -6,7 +6,7 @@
 #' For other types of posterior predictive checks for example with `bayesplot`, 
 #' you can extract the variable `yrep` from the output, see examples.
 #' 
-#' @importFrom bayesplot pp_check
+#' @importFrom bayesplot pp_check ppc_ribbon
 #' @param object An output from [walker()].
 #' @param ... Further parameters to [bayesplot::ppc_ribbon()].
 #' @export
@@ -14,10 +14,10 @@
 #' \dontrun{
 #' # Extracting the yrep variable for general use:
 #' # extract yrep
-#' y_rep <- rstan::extract(object$stanfit, pars = "y_rep", permuted = TRUE)$y_rep
+#' y_rep <- extract(object$stanfit, pars = "y_rep", permuted = TRUE)$y_rep
 #' 
 #' # For non-gaussian model:
-#' weights <- rstan::extract(object$stanfit, 
+#' weights <- extract(object$stanfit, 
 #' pars = "weights", permuted = TRUE)$weights
 #' y_rep <- y_rep[sample(1:nrow(y_rep), 
 #'   size = nrow(y_rep), replace = TRUE, prob = weights), , drop = FALSE]
diff --git a/man/pp_check.walker_fit.Rd b/man/pp_check.walker_fit.Rd
index 845ff0c..45e3a2f 100644
--- a/man/pp_check.walker_fit.Rd
+++ b/man/pp_check.walker_fit.Rd
@@ -22,10 +22,10 @@ you can extract the variable \code{yrep} from the output, see examples.
 \dontrun{
 # Extracting the yrep variable for general use:
 # extract yrep
-y_rep <- rstan::extract(object$stanfit, pars = "y_rep", permuted = TRUE)$y_rep
+y_rep <- extract(object$stanfit, pars = "y_rep", permuted = TRUE)$y_rep
 
 # For non-gaussian model:
-weights <- rstan::extract(object$stanfit, 
+weights <- extract(object$stanfit, 
 pars = "weights", permuted = TRUE)$weights
 y_rep <- y_rep[sample(1:nrow(y_rep), 
   size = nrow(y_rep), replace = TRUE, prob = weights), , drop = FALSE]
diff --git a/vignettes/walker.Rmd b/vignettes/walker.Rmd
index 7aca624..e52ef46 100644
--- a/vignettes/walker.Rmd
+++ b/vignettes/walker.Rmd
@@ -72,14 +72,13 @@ The output of `walker` is `walker_fit` object, which is essentially a list with
 
 ```{r pars}
 print(fit$stanfit, pars = c("sigma_y", "sigma_rw1"))
-library(bayesplot)
 mcmc_areas(as.matrix(fit$stanfit), regex_pars = c("sigma_y", "sigma_rw1"))
 ```
 
 Let's check how well our estimates of $\beta$ coincide with the true values (the solid lines correspond to true values):
 
 ```{r plot_with_true_betas}
-betas <- summary(fit$stanfit, "beta_rw")$summary[, "mean"]
+betas <- rstan::summary(fit$stanfit, "beta_rw")$summary[, "mean"]
 
 ts.plot(cbind(rw, beta1, beta2, matrix(betas, ncol = 3)),
   col = rep(1:3, 2), lty = rep(1:2, each = 3))
@@ -138,7 +137,7 @@ $$
 This is essentially local linear trend model [@harvey] with restriction that there is no noise on the $\beta$ level. This model can be estimated by switching `rw1` function inside of the walker formula to `rw2`, with almost identical interface, but now $\sigma$ correspond to the standard deviations of the slope terms $\nu$. The Gaussian prior for $\nu_1$ most also be defined. Using RW2 model, the coefficient estimates of our example model are clearly smoother:
 
 ```{r walker_rw2}
-fit_rw2 <-walker(y ~ -1 + 
+fit_rw2 <- walker(y ~ -1 + 
     rw2(~ x1 + x2, beta = c(0, 10), sigma = c(2, 0.001), nu = c(0, 10)), 
   refresh = 0, init = 0, chains = 1, sigma_y = c(2, 0.001))
 plot_coefs(fit_rw2, scales = "free") + ggplot2::theme_bw()