use fun() instead of fun across docs, fixes #383

R/boot.R

@@ -17,7 +17,7 @@
 #' @param times The number of bootstrap samples.
 #' @param apparent A logical. Should an extra resample be added where the
 #'  analysis and holdout subset are the entire data set. This is required for
-#'  some estimators used by the `summary` function that require the apparent
+#'  some estimators used by the `summary()` function that require the apparent
 #'  error rate.
 #' @export
 #' @return A tibble with classes `bootstraps`, `rset`, `tbl_df`, `tbl`, and

R/caret.R

@@ -4,10 +4,10 @@
 #'  \pkg{rsample} and \pkg{caret}.
 #'
 #' @param object An `rset` object. Currently,
-#'  `nested_cv` is not supported.
-#' @return `rsample2caret` returns a list that mimics the
+#'  `nested_cv()` is not supported.
+#' @return `rsample2caret()` returns a list that mimics the
 #'  `index` and `indexOut` elements of a
-#'  `trainControl` object. `caret2rsample` returns an
+#'  `trainControl` object. `caret2rsample()` returns an
 #'  `rset` object of the appropriate class.
 #' @export
 rsample2caret <- function(object, data = c("analysis", "assessment")) {
@@ -23,7 +23,7 @@ rsample2caret <- function(object, data = c("analysis", "assessment")) {
 }
 
 #' @rdname rsample2caret
-#' @param ctrl An object produced by `trainControl` that has
+#' @param ctrl An object produced by `caret::trainControl()` that has
 #'  had the `index` and `indexOut` elements populated by
 #'  integers. One method of getting this is to extract the
 #'  `control` objects from an object produced by `train`.

R/initial_split.R

@@ -1,18 +1,18 @@
 #' Simple Training/Test Set Splitting
 #'
-#' `initial_split` creates a single binary split of the data into a training
-#'  set and testing set. `initial_time_split` does the same, but takes the
+#' `initial_split()` creates a single binary split of the data into a training
+#'  set and testing set. `initial_time_split()` does the same, but takes the
 #'  _first_ `prop` samples for training, instead of a random selection.
-#'  `group_initial_split` creates splits of the data based
+#'  `group_initial_split()` creates splits of the data based
 #'  on some grouping variable, so that all data in a "group" is assigned to
 #'  the same split.
-#'  `training` and `testing` are used to extract the resulting data.
+#'  `training()` and `testing()` are used to extract the resulting data.
 #' @template strata_details
 #' @inheritParams vfold_cv
 #' @inheritParams make_strata
 #' @param prop The proportion of data to be retained for modeling/analysis.
 #' @export
-#' @return An `rsplit` object that can be used with the `training` and `testing`
+#' @return An `rsplit` object that can be used with the `training()` and `testing()`
 #'  functions to extract the data in each split.
 #' @examplesIf rlang::is_installed("modeldata")
 #' set.seed(1353)
@@ -176,12 +176,12 @@ group_initial_split <- function(data, group, prop = 3 / 4, ..., strata = NULL, p
   attrib <- .get_split_args(res, allow_strata_false = TRUE)
 
   res <- res$splits[[1]]
-  
+
   attrib$times <- NULL
   for (i in names(attrib)) {
     attr(res, i) <- attrib[[i]]
   }
   class(res) <- c("group_initial_split", "initial_split", class(res))
-  
+
   res
 }

R/labels.R

@@ -1,7 +1,7 @@
 #' Find Labels from rset Object
 #'
 #' Produce a vector of resampling labels (e.g. "Fold1") from
-#'  an `rset` object. Currently, `nested_cv`
+#'  an `rset` object. Currently, `nested_cv()`
 #'  is not supported.
 #'
 #' @param object An `rset` object

R/nest.R

@@ -1,6 +1,6 @@
 #' Nested or Double Resampling
 #'
-#' `nested_cv` can be used to take the results of one resampling procedure
+#' `nested_cv()` can be used to take the results of one resampling procedure
 #'   and conduct further resamples within each split. Any type of resampling
 #'   used in `rsample` can be used.
 #'

R/rsplit.R

@@ -66,8 +66,8 @@ as.integer.rsplit <-
 #'
 #' The analysis or assessment code can be returned as a data
 #'   frame (as dictated by the `data` argument) using
-#'   `as.data.frame.rsplit`. `analysis` and
-#'   `assessment` are shortcuts.
+#'   `as.data.frame.rsplit()`. `analysis()` and
+#'   `assessment()` are shortcuts.
 #' @param x An `rsplit` object.
 #' @param row.names `NULL` or a character vector giving the row names for the data frame. Missing values are not allowed.
 #' @param optional A logical: should the column names of the data be checked for legality?

R/tidy.R

@@ -1,6 +1,6 @@
 #' Tidy Resampling Object
 #'
-#' The `tidy` function from the \pkg{broom} package can be used on `rset` and
+#' The `tidy()` function from the \pkg{broom} package can be used on `rset` and
 #'  `rsplit` objects to generate tibbles with which rows are in the analysis and
 #'  assessment sets.
 #' @param x A  `rset` or  `rsplit` object
@@ -9,11 +9,11 @@
 #'  sample for the same row in the original data.
 #' @inheritParams rlang::args_dots_empty
 #' @return A tibble with columns `Row` and `Data`. The latter has possible
-#'  values "Analysis" or "Assessment". For `rset` inputs, identification columns
-#'  are also returned but their names and values depend on the type of
-#'  resampling. `vfold_cv` contains a column "Fold" and, if repeats are used,
-#'  another called "Repeats". `bootstraps` and `mc_cv` use the column
-#'  "Resample".
+#'   values "Analysis" or "Assessment". For `rset` inputs, identification
+#'   columns are also returned but their names and values depend on the type of
+#'   resampling. For `vfold_cv()`, contains a column "Fold" and, if repeats are
+#'   used, another called "Repeats". `bootstraps()` and `mc_cv()` use the column
+#'   "Resample".
 #' @details Note that for nested resampling, the rows of the inner resample,
 #'  named `inner_Row`, are *relative* row indices and do not correspond to the
 #'  rows in the original data set.

vignettes/Working_with_rsets.Rmd

@@ -109,7 +109,7 @@ example[1:10, setdiff(names(example), names(attrition))]
 
 For this model, the `.fitted` value is the linear predictor in log-odds units. 
 
-To compute this data set for each of the 100 resamples, we'll use the `map` function from the `purrr` package:
+To compute this data set for each of the 100 resamples, we'll use the `map()` function from the `purrr` package:
 
 ```{r model_purrr, warning=FALSE}
 library(purrr)
@@ -182,7 +182,7 @@ The calculated 95% confidence interval contains zero, so we don't have evidence
 
 ## Bootstrap Estimates of Model Coefficients
 
-Unless there is already a column in the resample object that contains the fitted model, a function can be used to fit the model and save all of the model coefficients. The [`broom` package](https://cran.r-project.org/package=broom) package has a `tidy` function that will save the coefficients in a data frame. Instead of returning a data frame with a row for each model term, we will save a data frame with a single row and columns for each model term. As before, `purrr::map` can be used to estimate and save these values for each split. 
+Unless there is already a column in the resample object that contains the fitted model, a function can be used to fit the model and save all of the model coefficients. The [`broom` package](https://cran.r-project.org/package=broom) package has a `tidy()` function that will save the coefficients in a data frame. Instead of returning a data frame with a row for each model term, we will save a data frame with a single row and columns for each model term. As before, `purrr::map()` can be used to estimate and save these values for each split. 
 
 
 ```{r coefs}