fix typos in doc

R/bayesplot-colors.R

@@ -23,7 +23,7 @@
 #'
 #' @return `color_scheme_set()` has the side effect of setting the color scheme
 #'   used for plotting. It also returns ([invisibly][base::invisible]) a list of
-#'   the hexidecimal color values used in `scheme`.
+#'   the hexadecimal color values used in `scheme`.
 #'
 #'   `color_scheme_get()` returns a list of the hexadecimal color
 #'   values (without changing the current scheme). If the `scheme` argument
@@ -61,7 +61,7 @@
 #' @section Custom color schemes: A **bayesplot** color scheme consists of six
 #'   colors. To specify a custom color scheme simply pass a character vector
 #'   containing either the names of six [colors][grDevices::colors] or six
-#'   hexidecimal color values (or a mix of names and hex values). The colors
+#'   hexadecimal color values (or a mix of names and hex values). The colors
 #'   should be in order from lightest to darkest. See the end of the
 #'   **Examples** section for a demonstration.
 #'
@@ -344,7 +344,7 @@ prepare_custom_colors <- function(scheme) {
   }
   if (length(not_found)) {
     abort(paste(
-      "Each color must specified as either a hexidecimal color value ",
+      "Each color must specified as either a hexadecimal color value ",
       "(e.g. '#C79999') or the name of a color (e.g. 'blue'). ",
       "The following provided colors were not found:",
       paste(unlist(not_found), collapse = ", ")

R/mcmc-combo.R

@@ -14,14 +14,14 @@
 #'   `widths = c(2, 1)` will allocate more space for the first column by a
 #'   factor of 2 (as would `widths = c(.3, .15)`, etc.). The default,
 #'   `NULL`, allocates the same horizontal space for each column.
-#' @param gg_theme Unlike most of the other **bayesplot**functions,
+#' @param gg_theme Unlike most of the other **bayesplot** functions,
 #'   `mcmc_combo` returns a gtable object rather than a ggplot object, and
 #'   so theme objects can't be added directly to the returned plot object. The
 #'   `gg_theme` argument helps get around this problem by accepting a
 #'   **ggplot2** [theme][ggplot2::theme] object that is added to each of the
 #'   plots *before* combining them into the gtable object that is returned.
 #'   This can be a theme object created by a call to [ggplot2::theme()] or
-#'   one of the **bayesplot**convenience functions, e.g.
+#'   one of the **bayesplot** convenience functions, e.g.
 #'   [legend_none()] (see the **Examples** section, below).
 #'
 #' @return A gtable object (the result of calling

R/ppc-overview.R

@@ -75,10 +75,10 @@
 #'   overlaid. The x-axis variable can be optionally specified by the user
 #'   (e.g. to plot against against a predictor variable or over time).
 #' * [__Predictive errors__][PPC-errors]: Plots of predictive errors
-#'   (`y - yrep`) computed from `y` and eachc of the simulated datasets (rows)
+#'   (`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
-#'   visualizations) of the data `y` vs. individual simuluted datasets
+#'   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

vignettes/visual-mcmc-diagnostics.Rmd

@@ -170,7 +170,7 @@ something is wrong with the model and the results should not be trusted.
 For an explanation of these warnings see 
 [Divergent transitions after warmup](https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup).
 We'll have a look at diagnosing the source of the divergences first and then
-dive into some diagnostics that should be checked even if there are no warnigns
+dive into some diagnostics that should be checked even if there are no warnings
 from the sampler.
 
 <br>
@@ -242,7 +242,7 @@ MCMC plotting functions demonstrated in the
 [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html)
 vignette also take optional arguments that can be used to display important
 HMC/NUTS diagnostic information. We'll see examples of this in the next
-section on divergenent transitions.
+section on divergent transitions.
 
 
 ### Divergent transitions
@@ -603,7 +603,7 @@ The **bayesplot** package provides the functions `mcmc_rhat` and
 `mcmc_rhat_hist` for visualizing $\hat{R}$ estimates.
 
 First we'll quickly fit one of the models above again, this time intentionally
-using too few MCMC iterations and allowing more dispered initial values. 
+using too few MCMC iterations and allowing more dispersed initial values. 
 This should lead to some high $\hat{R}$ values.
 
 ```{r, results='hide'}