try fixing winbuilder errors

Author: jgabry

DESCRIPTION

@@ -2,7 +2,7 @@ Package: bayesplot
 Type: Package
 Title: Plotting for Bayesian Models
 Version: 1.7.1
-Date: 2019-11-25
+Date: 2019-11-28
 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "jsg2201@columbia.edu"),
              person("Tristan", "Mahr", role = "aut"),
              person("Paul-Christian", "Bürkner", role = "ctb"),

R/mcmc-intervals.R

@@ -60,7 +60,7 @@
 #' color_scheme_set("brightblue")
 #' mcmc_intervals(x)
 #' mcmc_intervals(x, pars = c("beta[1]", "beta[2]"))
-#' mcmc_areas(x, regex_pars = "beta\\[[1-3]", prob = 0.8) +
+#' mcmc_areas(x, regex_pars = "beta\\[[1-3]\\]",  prob = 0.8) +
 #'  ggplot2::labs(
 #'    title = "Posterior distributions",
 #'    subtitle = "with medians and 80% intervals"

R/ppc-overview.R

@@ -64,28 +64,28 @@
 #' @section PPC plotting functions: The plotting functions for prior and
 #'   posterior predictive checking are organized into several categories, each
 #'   with its own documentation:
-#' * [__Distributions__][PPC-distributions]: Histograms, kernel density
+#' * [Distributions][PPC-distributions]: Histograms, kernel density
 #'   estimates, boxplots, and other plots comparing the empirical distribution
 #'   of data `y` to the distributions of individual simulated datasets (rows)
 #'   in `yrep`.
-#' * [__Statistics__][PPC-test-statistics]: The distribution of a statistic,
+#' * [Statistics][PPC-test-statistics]: The distribution of a statistic,
 #'   or a pair of statistics, over the simulated datasets (rows) in `yrep`
 #'   compared to value of the statistic(s) computed from `y`.
-#' * [__Intervals__][PPC-intervals]: Interval estimates of `yrep` with `y`
+#' * [Intervals][PPC-intervals]: Interval estimates of `yrep` with `y`
 #'   overlaid. The x-axis variable can be optionally specified by the user
 #'   (e.g. to plot against a predictor variable or over time).
-#' * [__Predictive errors__][PPC-errors]: Plots of predictive errors
+#' * [Predictive errors][PPC-errors]: Plots of predictive errors
 #'   (`y - yrep`) computed from `y` and each of the simulated datasets (rows)
 #'   in `yrep`. For binomial models binned error plots are also available.
-#' * [__Scatterplots__][PPC-scatterplots]: Scatterplots (and similar
+#' * [Scatterplots][PPC-scatterplots]: Scatterplots (and similar
 #'   visualizations) of the data `y` vs. individual simulated datasets
 #'   (rows) in `yrep`, or vs. the average value of the distributions of each
 #'   data point (columns) in `yrep`.
-#' * [__Plots for discrete outcomes__][PPC-discrete]: PPC functions that can
+#' * [Plots for discrete outcomes][PPC-discrete]: PPC functions that can
 #'   only be used if `y` and `yrep` are discrete. For example, rootograms for
 #'   count outcomes and bar plots for ordinal, categorical, and
 #'   multinomial outcomes.
-#' * [__LOO predictive checks__][PPC-loo]: PPC functions for predictive checks
+#' * [LOO predictive checks][PPC-loo]: PPC functions for predictive checks
 #'   based on (approximate) leave-one-out (LOO) cross-validation.
 #'
 #' @section Providing an interface for predictive checking from another package:

inst/CITATION

@@ -8,7 +8,7 @@ bibentry(bibtype = "Misc",
                     ),
          year = year,
          note = note,
-         url = c("mc-stan.org/bayesplot"),
+         url = c("https://mc-stan.org/bayesplot"),
          header = "To cite the bayesplot R package:"
 )
 
@@ -25,5 +25,5 @@ bibentry(bibtype = "Article",
          issue = 2,
          pages = "389-402",
          doi = "10.1111/rssa.12378",
-         header = "To cite the 'Visualization in Bayesian workflow' paper:"
+         header = "To cite the bayesplot paper 'Visualization in Bayesian workflow':"
 )