fix cran issues

DESCRIPTION

@@ -64,10 +64,10 @@ Suggests:
     transformr
 License: GPL (>= 3)
 Language: en-US
-BugReports: https://github.com/mjskay/tidybayes/issues/new
+BugReports: https://github.com/mjskay/tidybayes/issues
 URL: https://mjskay.github.io/tidybayes/, https://github.com/mjskay/tidybayes/
 VignetteBuilder: knitr
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 LazyData: true
 Encoding: UTF-8
 Collate:

R/compose_data.R

@@ -221,7 +221,7 @@ as_data_list.data_list = function(object, name="", ...) {
 #'      \item all other types are dropped (and a warning given)
 #' }
 #'
-#' As in functions like [mutate()], each expression is evaluated in an
+#' As in functions like [dplyr::mutate()], each expression is evaluated in an
 #' environment containing the data list built up so far.
 #'
 #' For example, this means that if the first argument to `compose_data`

R/nest_rvars.R

@@ -15,17 +15,17 @@
 #'    and `.iteration` columns) indexing draws. It should be a grouped by any
 #'    columns that are not intended to be nested.
 #'  - For `unnest_rvars()`, the data frame should have at least one column that
-#'    is an [`rvar`]; all `rvar` columns will be unnested.
+#'    is an [`rvar`][posterior::rvar]; all `rvar` columns will be unnested.
 #'
 #' @return
 #'
 #' For `nest_rvars()`, returns a data frame without `.chain`, `.iteration`, and
-#' `.draw` columns, where all non-grouped columns have been converted to [`rvar`]s.
+#' `.draw` columns, where all non-grouped columns have been converted to [`rvar`][posterior::rvar]s.
 #'
 #' For `unnest_rvars()`, returns a data frame with `.chain`, `.iteration`, and
-#' `.draw` columns added, where every [`rvar`] column in the input has been
-#' converted to (one or more) columns containing draws from those [`rvar`]s
-#' in long format. The result is grouped by all non-[`rvar`] columns in the
+#' `.draw` columns added, where every [`rvar`][posterior::rvar] column in the input has been
+#' converted to (one or more) columns containing draws from those [`rvar`][posterior::rvar]s
+#' in long format. The result is grouped by all non-[`rvar`][posterior::rvar] columns in the
 #' input; this ensures that `nest_rvars(unnest_rvars(x))` returns `x`.
 #'
 #' @examples

R/predict_curve.R

@@ -16,7 +16,7 @@
 #' The function generates a predictive curve given posterior draws
 #' (`data`), an expression (`formula`), and a set of variables
 #' defining the curve (`...`). For every group in `data` (if it is a
-#' grouped data frame---see [group_by()]; otherwise the entire data
+#' grouped data frame---see [group_by()][dplyr::group_by]; otherwise the entire data
 #' frame is taken at once), and for each combination of values in `...`,
 #' the right-hand side of `formula` is evaluated and its results passed to
 #' the `summary` function. This allows a predictive curve to be generated,
@@ -33,10 +33,10 @@
 #' convenient argument for adjusting the number of bins per point on the
 #' curve.
 #'
-#' @param data A [data.frame], [tbl_df] or
-#' [grouped_df] representing posteriors from a Bayesian model as
+#' @param data A [`data.frame`], [`tibble`][tibble::tibble], or
+#' [`grouped_df`][dplyr::grouped_df] representing posteriors from a Bayesian model as
 #' might be obtained through [spread_draws()]. Grouped data frames
-#' as returned by [group_by()] are supported.
+#' as returned by [group_by()][dplyr::group_by] are supported.
 #' @param formula A formula specifying the prediction curve. The left-hand side
 #' of the formula should be a name representing the name of the column that
 #' will hold the predicted response in the returned data frame. The right-hand

R/predicted_rvars.R

@@ -8,12 +8,12 @@
 
 #' Add `rvar`s for the linear predictor, posterior expectation, posterior predictive, or residuals of a model to a data frame
 #'
-#' Given a data frame and a model, adds [`rvar`]s of draws from the linear/link-level predictor,
+#' Given a data frame and a model, adds [`rvar`][posterior::rvar]s of draws from the linear/link-level predictor,
 #' the expectation of the posterior predictive, or the posterior predictive to
 #' the data frame.
 #'
 #' @templateVar pred_type rvars
-#' @templateVar draws [`rvar`]s containing draws
+#' @templateVar draws [`rvar`][posterior::rvar]s containing draws
 #' @template details-pred
 #' @template param-pred-newdata
 #' @template param-pred-object
@@ -32,10 +32,10 @@
 #' correspond to the same row in `newdata`.
 #' See `vignette("tidy-posterior")` for examples of dealing with output ordinal models.
 #' @return A data frame (actually, a [tibble][tibble::tibble]) equal to the input `newdata` with
-#' additional columns added containing [`rvar`]s representing the requested predictions or fits.
+#' additional columns added containing [`rvar`][posterior::rvar]s representing the requested predictions or fits.
 #' @author Matthew Kay
 #' @seealso [add_predicted_draws()] for the analogous functions that use a long-data-frame-of-draws
-#' format instead of a data-frame-of-`rvar`s format. See [spread_rvars()] for manipulating posteriors directly.
+#' format instead of a data-frame-of-[`rvar`][posterior::rvar]s format. See [spread_rvars()] for manipulating posteriors directly.
 #' @keywords manip
 #' @examples
 #' \dontrun{

R/spread_draws.R

@@ -531,7 +531,7 @@ nest_dimensions_ = function(
     }
 
     if (reindex) {
-      is_character_index = is.character(indices)
+      is_character_index = is.character(indices) || is.factor(indices)
 
       #create a template list that we can use to re-index the values
       template_vector = long_draws[[value_name]][[1]][[1]]

R/spread_rvars.R

@@ -18,7 +18,7 @@
 #' they will have multiple columns with names like `"b[1,1]"`, `"b[2,1]"`, etc.
 #'
 #' `spread_rvars` and `gather_rvars` provide a straightforward
-#' syntax to translate these columns back into properly-indexed [`rvar`]s in two different
+#' syntax to translate these columns back into properly-indexed [`rvar`][posterior::rvar]s in two different
 #' tidy data frame formats, optionally recovering dimension types (e.g. factor levels) as it does so.
 #'
 #' `spread_rvars` will spread names of variables in the model across the data frame as column names,
@@ -31,8 +31,8 @@
 #' \itemize{
 #'    \item column `"i"`: value in `1:5`
 #'    \item column `"v"`: value in `1:10`
-#'    \item column `"a"`: [rvar] containing draws from `"a[i]"`
-#'    \item column `"b"`: [rvar] containing draws from `"b[i,v]"`
+#'    \item column `"a"`: [`rvar`][posterior::rvar] containing draws from `"a[i]"`
+#'    \item column `"b"`: [`rvar`][posterior::rvar] containing draws from `"b[i,v]"`
 #'  }
 #'
 #' `gather_rvars(model, a[i], b[i,v])` on the same model would return a data frame with:
@@ -41,7 +41,7 @@
 #'    \item column `"v"`: value in `1:10`, or `NA`
 #'      on rows where `".variable"` is `"a"`.
 #'    \item column `".variable"`: value in `c("a", "b")`.
-#'    \item column `".value"`: [rvar] containing draws from `"a[i]"` (when `".variable"` is `"a"`)
+#'    \item column `".value"`: [`rvar`][posterior::rvar] containing draws from `"a[i]"` (when `".variable"` is `"a"`)
 #'      or `"b[i,v]"` (when `".variable"` is `"b"`)
 #'  }
 #'
@@ -100,7 +100,7 @@
 #' Besides being more compact, the `c()`-style syntax is currently also slightly
 #' faster (though that may change).
 #'
-#' Dimensions can be left nested in the resulting [rvar] objects by leaving their names
+#' Dimensions can be left nested in the resulting [`rvar`][posterior::rvar] objects by leaving their names
 #' blank; e.g. `spread_rvars(model, b[i,])` will place the first index (`i`) into
 #' rows of the data frame but leave the second index nested in the `b` column
 #' (see *Examples* below).

R/testthat.R

@@ -3,6 +3,15 @@
 # Author: mjskay
 ###############################################################################
 
+
+# skips -------------------------------------------------------------------
+
+#' skip tests sensitive to minor changes in density() in R 4.4
+#' @noRd
+skip_if_sensitive_to_density = function() {
+  testthat::skip_if(getRversion() < "4.4", "density() output changed in R 4.4")
+}
+
 # skip tests if there is no vdiffr setup or if the setup is likely
 # to produce false positive test failures (e.g. old version of ggplot2)
 skip_if_no_vdiffr = function() {