add release_questions() helper

[ci skip]

.Rbuildignore

@@ -13,3 +13,4 @@ images/*
 book/*
 docs/*
 Rplots.pdf
+tests/figs/*

R/bayesplot-package.R

@@ -91,3 +91,13 @@
 #' }
 #'
 NULL
+
+
+# internal ----------------------------------------------------------------
+
+# release reminders (for devtools)
+release_questions <- function() { # nocov start
+  c(
+    "Have you reduced the size of the vignettes for CRAN?",
+  )
+} # nocov end

vignettes/plotting-mcmc-draws.Rmd

@@ -166,10 +166,18 @@ transform the draws in advance or use the `transformations` argument.
 
 ```{r, mcmc_hist-transform, message=FALSE}
 color_scheme_set("blue")
-mcmc_hist(posterior, pars = c("wt", "sigma"), 
+mcmc_hist(posterior, pars = c("wt", "sigma"),
           transformations = list("sigma" = "log"))
 ```
 
+<!-- ```{r, mcmc_hist-transform, message=FALSE, eval=FALSE} -->
+<!-- # not evaluated to reduce vignette size for CRAN -->
+<!-- # full version available at mc-stan.org/bayesplot/articles -->
+<!-- color_scheme_set("blue") -->
+<!-- mcmc_hist(posterior, pars = c("wt", "sigma"),  -->
+<!--           transformations = list("sigma" = "log")) -->
+<!-- ``` -->
+
 Most of the other functions for plotting MCMC draws also have a
 `transformations` argument.
 
@@ -267,6 +275,14 @@ mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"),
            off_diag_args = list(size = 1.5))
 ```
 
+<!-- ```{r, mcmc_pairs, message=FALSE, eval=FALSE} -->
+<!-- # not evaluated to reduce vignette size for CRAN -->
+<!-- # full version available at mc-stan.org/bayesplot/articles -->
+<!-- color_scheme_set("pink") -->
+<!-- mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"), -->
+<!--            off_diag_args = list(size = 1.5)) -->
+<!-- ``` -->
+
 The univariate marginal posteriors are shown along the diagonal as histograms, 
 but this can be changed to densities by setting `diag_fun="dens"`. Bivariate
 plots are displayed above and below the diagonal as scatterplots, but it is also
@@ -325,11 +341,18 @@ by side, and `strip.position="left"` moves the facet labels to the y-axis
 The [`"viridis"` color scheme](https://CRAN.R-project.org/package=viridis) is
 also useful for trace plots because it is comprised of very distinct colors:
 
-```{r, viridis-scheme}
+```{r, viridis-scheme, eval=FALSE}
 color_scheme_set("viridis")
 mcmc_trace(posterior, pars = "(Intercept)")
 ```
 
+<!-- ```{r, viridis-scheme, eval=FALSE} -->
+<!-- # not evaluated to reduce vignette size for CRAN -->
+<!-- # full version available at mc-stan.org/bayesplot/articles -->
+<!-- color_scheme_set("viridis") -->
+<!-- mcmc_trace(posterior, pars = "(Intercept)") -->
+<!-- ``` -->
+
 #### mcmc_trace_highlight
 
 The `mcmc_trace_highlight` function uses points instead of lines and reduces the

vignettes/visual-mcmc-diagnostics.Rmd

@@ -253,21 +253,27 @@ visualizations.
 
 #### mcmc_parcoord
 
-The parallel coordinates plot (`mcmc_parcoord`) is probably the first plot to
-have look at if you have no idea, where the divergences in your model might be
-coming from. This function works in general without including information about
-the divergences, but if the optional `np` argument is used to pass NUTS
-parameter information, then divergences will be colored in the plot (by default
-in red).
+The `mcmc_parcoord` plot shows one line per iteration, connecting the parameter
+values at this iteration. This lets you see global patterns in the divergences.
+
+This function works in general without including information about the
+divergences, but if the optional `np` argument is used to pass NUTS parameter
+information, then divergences will be colored in the plot (by default in red).
 
 ```{r, mcmc_parcoord-1}
 color_scheme_set("darkgray")
 mcmc_parcoord(posterior_cp, np = np_cp)
 ```
 
-The `mcmc_parcoord` plot shows one line per iteration, connecting the parameter
-values at this iteration. This lets you see any global patterns in the
-divergences. Here, you may notice that divergences in the centered
+
+<!-- ```{r, mcmc_parcoord-1, eval=FALSE} -->
+<!-- # not evaluated to reduce vignette size for CRAN -->
+<!-- # full version available at mc-stan.org/bayesplot/articles -->
+<!-- color_scheme_set("darkgray") -->
+<!-- mcmc_parcoord(posterior_cp, np = np_cp) -->
+<!-- ``` -->
+
+Here, you may notice that divergences in the centered
 parameterization happen exclusively when  `tau`, the hierarchical standard
 deviation, goes near zero and the values of the `theta`s are essentially fixed.
 This makes `tau` immediately suspect. 
@@ -289,10 +295,17 @@ Let's look at how `tau` interacts with other variables, using only one of the
 `theta`s to keep the plot readable:
 
 ```{r, mcmc_pairs}
-mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"), 
+mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"),
            off_diag_args = list(size = 0.75))
 ```
 
+<!-- ```{r, mcmc_pairs, eval=FALSE} -->
+<!-- # not evaluated to reduce vignette size for CRAN -->
+<!-- # full version available at mc-stan.org/bayesplot/articles -->
+<!-- mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"),  -->
+<!--            off_diag_args = list(size = 0.75)) -->
+<!-- ``` -->
+
 Note that each bivariate plot is present twice -- by default each of those
 contain half of the chains, so you also get to see if the chains produced
 similar results (see the documentation for the `condition` argument for