diff --git a/R/initial_split.R b/R/initial_split.R
index 51d2c5cd..c754e424 100644
--- a/R/initial_split.R
+++ b/R/initial_split.R
@@ -1,20 +1,20 @@
 #' 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.
-#'
-#' @details `training` and `testing` are used to extract the resulting data.
+#'  the same split. 
+#' 
+#' @details `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)
diff --git a/R/initial_validation_split.R b/R/initial_validation_split.R
index 70d40ec8..0aec03fd 100644
--- a/R/initial_validation_split.R
+++ b/R/initial_validation_split.R
@@ -9,9 +9,9 @@
 #' based on some grouping variable, so that all data in a "group" are assigned
 #' to the same partition.
 #'
-#' @details `training()`, `validation()`, and `testing()` can be used to extract the
+#' @details [training()], [validation()], and [testing()] can be used to extract the
 #' resulting data sets.
-#' Use [`validation_set()`] to create an `rset` object for use with functions from
+#' Use [validation_set()] to create an `rset` object for use with functions from
 #' the tune package such as `tune::tune_grid()`.
 #'
 #' @template strata_details
diff --git a/R/validation_set.R b/R/validation_set.R
index 2ec79da7..db775e02 100644
--- a/R/validation_set.R
+++ b/R/validation_set.R
@@ -1,6 +1,6 @@
 #' Create a Validation Split for Tuning
 #'
-#' `validation_set` creates a the validation split for model tuning.
+#' `validation_set()` creates a the validation split for model tuning.
 #'
 #' @param split An object of class `initial_validation_split`, such as resulting
 #' from [initial_validation_split()] or [group_initial_validation_split()].
diff --git a/man/initial_split.Rd b/man/initial_split.Rd
index 1e8946f2..07fc96b6 100644
--- a/man/initial_split.Rd
+++ b/man/initial_split.Rd
@@ -61,19 +61,19 @@ grouping observations with the same value to either the analysis or
 assessment set within a fold.}
 }
 \value{
-An \code{rsplit} object that can be used with the \code{training} and \code{testing}
+An \code{rsplit} object that can be used with the \code{training()} and \code{testing()}
 functions to extract the data in each split.
 }
 \description{
-\code{initial_split} creates a single binary split of the data into a training
-set and testing set. \code{initial_time_split} does the same, but takes the
+\code{initial_split()} creates a single binary split of the data into a training
+set and testing set. \code{initial_time_split()} does the same, but takes the
 \emph{first} \code{prop} samples for training, instead of a random selection.
-\code{group_initial_split} creates splits of the data based
+\code{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.
 }
 \details{
-\code{training} and \code{testing} are used to extract the resulting data.
+\code{training()} and \code{testing()} are used to extract the resulting data.
 
 With a \code{strata} argument, the random sampling is conducted
 \emph{within the stratification variable}. This can help ensure that the
diff --git a/man/initial_validation_split.Rd b/man/initial_validation_split.Rd
index 0b9d5be8..9017e36e 100644
--- a/man/initial_validation_split.Rd
+++ b/man/initial_validation_split.Rd
@@ -83,7 +83,7 @@ based on some grouping variable, so that all data in a "group" are assigned
 to the same partition.
 }
 \details{
-\code{training()}, \code{validation()}, and \code{testing()} can be used to extract the
+\code{\link[=training]{training()}}, \code{\link[=validation]{validation()}}, and \code{\link[=testing]{testing()}} can be used to extract the
 resulting data sets.
 Use \code{\link[=validation_set]{validation_set()}} to create an \code{rset} object for use with functions from
 the tune package such as \code{tune::tune_grid()}.
diff --git a/man/validation_set.Rd b/man/validation_set.Rd
index b5e3fc2a..98de1f31 100644
--- a/man/validation_set.Rd
+++ b/man/validation_set.Rd
@@ -35,7 +35,7 @@ An tibble with classes \code{validation_set}, \code{rset}, \code{tbl_df}, \code{
 column called \code{id} that has a character string with the resample identifier.
 }
 \description{
-\code{validation_set} creates a the validation split for model tuning.
+\code{validation_set()} creates a the validation split for model tuning.
 }
 \examples{
 set.seed(1353)