fix URLs from r cmd check NOTE

DESCRIPTION

@@ -2,7 +2,7 @@ Package: bayesplot
 Type: Package
 Title: Plotting for Bayesian Models
 Version: 1.8.0
-Date: 2021-01-06
+Date: 2021-01-07
 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "[email protected]"),
              person("Tristan", "Mahr", role = "aut"),
              person("Paul-Christian", "Bürkner", role = "ctb"),
@@ -22,7 +22,7 @@ Description: Plotting functions for posterior analysis, MCMC diagnostics,
     particularly (but not exclusively) packages interfacing with 'Stan'.
 License: GPL (>= 3)
 LazyData: TRUE
-URL: https://mc-stan.org/bayesplot
+URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
 SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
 Depends:

NEWS.md

@@ -81,7 +81,7 @@ matrices also inheriting from "array" in R 4.0.
 * Added `mcmc_trace_data()`, which returns the data used for plotting the trace
   plots and rank histograms. (Advances #97)
 
-* [ColorBrewer](http://colorbrewer2.org) palettes are now available as color
+* [ColorBrewer](https://colorbrewer2.org/) palettes are now available as color
   schemes via
   [`color_scheme_set()`](https://mc-stan.org/bayesplot/reference/bayesplot-colors.html).
   For example, `color_scheme_set("brewer-Spectral")` will use the Spectral

R/bayesplot-package.R

@@ -44,7 +44,7 @@
 #'
 #' @section Resources:
 #' * __Online documentation and vignettes__: Visit the __bayesplot__ website at
-#'   <https://mc-stan.org/bayesplot>
+#'   <https://mc-stan.org/bayesplot/>
 #' * __Bug reports and feature requests__: If you would like to request a new
 #'   feature or if you have noticed a bug that needs to be fixed please let us
 #'   know at the **bayesplot** issue tracker at

R/ppc-censoring.R

@@ -5,12 +5,11 @@
 #'   predictive distribution. See the **Plot Descriptions** section, below, for
 #'   details.
 #'
-#'   Although some of the other plots can be used with censored data,
-#'   `ppc_km_overlay()` is currently the only plotting function designed
+#'   Although some of the other \pkg{bayesplot} plots can be used with censored
+#'   data, `ppc_km_overlay()` is currently the only plotting function designed
 #'   *specifically* for censored data. We encourage you to suggest or contribute
-#'   additional plots at [https://github.com/stan-dev/bayesplot](github.com/stan-dev/bayesplot).
-#'
-#'
+#'   additional plots at
+#'   [https://github.com/stan-dev/bayesplot](github.com/stan-dev/bayesplot).
 #'
 #' @name PPC-censoring
 #' @family PPCs
@@ -26,10 +25,10 @@
 #' \describe{
 #'   \item{`ppc_km_overlay()`}{
 #'    Empirical CCDF estimates of each dataset (row) in `yrep` are overlaid,
-#'    with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for `y` itself
-#'    on top (and in a darker shade). This is a PPC suitable for
-#'    right-censored `y`. Note that the replicated data from `yrep` is assumed
-#'    to be uncensored.
+#'    with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for `y` itself on
+#'    top (and in a darker shade). This is a PPC suitable for right-censored
+#'    `y`. Note that the replicated data from `yrep` is assumed to be
+#'    uncensored.
 #'   }
 #' }
 #'
@@ -74,11 +73,9 @@ ppc_km_overlay <-
       abort("Package 'ggfortify' required.")
     }
 
-    # Checks for 'status_y':
     stopifnot(is.numeric(status_y))
     stopifnot(all(status_y %in% c(0, 1)))
 
-    # Create basic PPC dataset:
     data <- ppc_data(y, yrep, group = status_y)
 
     # Modify the status indicator:
@@ -93,23 +90,20 @@ ppc_km_overlay <-
                                    as.numeric(as.character(.data$group)),
                                    1))
 
-    # Create 'survfit' object and 'fortify' it
     sf <- survival::survfit(
       survival::Surv(value, group) ~ rep_label,
       data = data
     )
     fsf <- fortify(sf)
 
-    # Add variables specifying color, size, and alpha:
     fsf$is_y_color <- as.factor(sub("\\[rep\\] \\(.*$", "rep", sub("^italic\\(y\\)", "y", fsf$strata)))
     fsf$is_y_size <- ifelse(fsf$is_y_color == "yrep", size, 1)
     fsf$is_y_alpha <- ifelse(fsf$is_y_color == "yrep", alpha, 1)
 
     # Ensure that the observed data gets plotted last by reordering the
-    # levels of the factor "strata":
+    # levels of the factor "strata"
     fsf$strata <- factor(fsf$strata, levels = rev(levels(fsf$strata)))
 
-    # Plot:
     ggplot(data = fsf,
            mapping = aes_(x = ~ time,
                           y = ~ surv,

inst/CITATION

@@ -8,7 +8,7 @@ bibentry(bibtype = "Misc",
                     ),
          year = year,
          note = note,
-         url = c("https://mc-stan.org/bayesplot"),
+         url = c("https://mc-stan.org/bayesplot/"),
          header = "To cite the bayesplot R package:"
 )
 

man-roxygen/reference-loo.R

@@ -2,4 +2,4 @@
 #'   Bayesian model evaluation using leave-one-out cross-validation and WAIC.
 #'   *Statistics and Computing*. 27(5), 1413--1432.
 #'   doi:10.1007/s11222-016-9696-4. arXiv preprint:
-#'   <https://arxiv.org/abs/1507.04544/>
+#'   <https://arxiv.org/abs/1507.04544>

vignettes/graphical-ppcs.Rmd

@@ -392,7 +392,7 @@ version 1.7.0. https://CRAN.R-project.org/package=brms
 
 Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression 
 Modeling via Stan. R package version 2.15.3. 
-https://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
+https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm
 
 Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), 
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 

vignettes/plotting-mcmc-draws.Rmd

@@ -370,7 +370,7 @@ mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3)
 
 Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression 
 Modeling via Stan. R package version 2.15.3. 
-https://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
+https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm
 
 Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019),
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 

vignettes/visual-mcmc-diagnostics.Rmd

@@ -448,7 +448,7 @@ us diagnose divergences. We may however zoom in to investigate, using the
 
 ```{r echo=FALSE}
 #A check that the chosen window still relevant
-n_divergent_in_window <- np_cp %>% filter(Parameter == "divergent__" & Value == 1 & Iteration >= 700 & Iteration <= 900) %>% nrow()
+n_divergent_in_window <- np_cp %>% filter(Parameter == "divergent__" & Value == 1 & Iteration >= 300 & Iteration <= 500) %>% nrow()
 
 if(n_divergent_in_window < 6) {
   divergences <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% select(Iteration) %>% get("Iteration", .) %>% sort() %>% paste(collapse = ",")
@@ -457,7 +457,7 @@ if(n_divergent_in_window < 6) {
 ```
 
 ```{r mcmc_trace_zoom}
-mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(700,900)) + 
+mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(300,500)) + 
   xlab("Post-warmup iteration")
 ```
 
@@ -783,7 +783,7 @@ models. https://arxiv.org/abs/1312.0906
 
 Gabry, J., and Goodrich, B. (2018). rstanarm: Bayesian Applied Regression
 Modeling via Stan. R package version 2.17.4.
-https://mc-stan.org/rstanarm
+https://mc-stan.org/rstanarm/
 
 Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019),
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 
@@ -810,4 +810,4 @@ Stan Development Team. _Stan Modeling Language Users
 Guide and Reference Manual_. https://mc-stan.org/users/documentation/
 
 Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.17.3.
-https://mc-stan.org/rstan
+https://mc-stan.org/rstan/