diff --git a/NEWS.md b/NEWS.md
index d5814b8f..ed7ab225 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -12,6 +12,8 @@
 
 * Formatting improvement: package names are now not in backticks anymore (@agmurray, #525).
 
+* Improved documentation and formatting: function names are now more easily identifiable through either `()` at the end or being links to the function documentation (@brshallo , #521).
+
 ## Bug fixes
 
 * `vfold_cv()` now utilizes the `breaks` argument correctly for repeated cross-validation (@ZWael, #471).
diff --git a/R/boot.R b/R/boot.R
index 887de4bd..edb40593 100644
--- a/R/boot.R
+++ b/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
diff --git a/R/caret.R b/R/caret.R
index 6b2808b3..26f66fcc 100644
--- a/R/caret.R
+++ b/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`.
diff --git a/R/form_pred.R b/R/form_pred.R
index 3d166c37..77348511 100644
--- a/R/form_pred.R
+++ b/R/form_pred.R
@@ -1,6 +1,6 @@
 #' Extract Predictor Names from Formula or Terms
 #'
-#' `all.vars` returns all variables used in a formula. This
+#' While [all.vars()] returns all variables used in a formula, this
 #'  function only returns the variables explicitly used on the
 #'  right-hand side (i.e., it will not resolve dots unless the
 #'  object is terms with a data set specified).
diff --git a/R/labels.R b/R/labels.R
index 53e7bfe6..becce98e 100644
--- a/R/labels.R
+++ b/R/labels.R
@@ -1,10 +1,9 @@
 #' Find Labels from rset Object
 #'
 #' Produce a vector of resampling labels (e.g. "Fold1") from
-#'  an `rset` object. Currently, `nested_cv`
-#'  is not supported.
+#' an `rset` object. Currently, [nested_cv()] is not supported.
 #'
-#' @param object An `rset` object
+#' @param object An `rset` object.
 #' @param make_factor A logical for whether the results should be
 #'  a character or a factor.
 #' @param ... Not currently used.
@@ -68,7 +67,7 @@ labels.rsplit <- function(object, ...) {
 #' For a data set, `add_resample_id()` will add at least one new column that
 #'  identifies which resample that the data came from. In most cases, a single
 #'  column is added but for some resampling methods, two or more are added.
-#' @param .data A data frame
+#' @param .data A data frame.
 #' @param split A single `rset` object.
 #' @param dots A single logical: should the id columns be prefixed with a "."
 #'  to avoid name conflicts with `.data`?
diff --git a/R/make_groups.R b/R/make_groups.R
index d5dabf24..47f43021 100644
--- a/R/make_groups.R
+++ b/R/make_groups.R
@@ -25,7 +25,7 @@
 #'  only one) assessment set, but rather allow each observation to be in an
 #'  assessment set zero-or-more times. As a result, those functions don't have
 #'  a `balance` argument, and under the hood always specify `balance = "prop"`
-#'  when they call [make_groups()].
+#'  when they call `make_groups()`.
 #'
 #' @keywords internal
 make_groups <- function(data,
diff --git a/R/nest.R b/R/nest.R
index a1c683e0..87e0579f 100644
--- a/R/nest.R
+++ b/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.
 #'
diff --git a/R/permutations.R b/R/permutations.R
index e5a42a13..c9702e48 100644
--- a/R/permutations.R
+++ b/R/permutations.R
@@ -5,7 +5,7 @@
 #'   by permuting/shuffling one or more columns. This results in analysis
 #'   samples where some columns are in their original order and some columns
 #'   are permuted to a random order. Unlike other sampling functions in
-#'   rsample, there is no assessment set and calling `assessment()` on a
+#'   rsample, there is no assessment set and calling [assessment()] on a
 #'   permutation split will throw an error.
 #'
 #' @param data A data frame.
diff --git a/R/printing.R b/R/printing.R
index d182597c..b456c12e 100644
--- a/R/printing.R
+++ b/R/printing.R
@@ -1,4 +1,4 @@
-## The `pretty` methods below are good for when you need to
+## The `pretty()` methods below are good for when you need to
 ## textually describe the resampling procedure. Note that they
 ## can have more than one element (in the case of nesting)
 
diff --git a/R/reg_intervals.R b/R/reg_intervals.R
index 50a0cf44..e820ab77 100644
--- a/R/reg_intervals.R
+++ b/R/reg_intervals.R
@@ -2,18 +2,18 @@
 #'
 #' @param formula An R model formula with one outcome and at least one predictor.
 #' @param data A data frame.
-#' @param model_fn The model to fit. Allowable values are "lm", "glm",
-#'  "survreg", and "coxph". The latter two require that the `survival` package
+#' @param model_fn The model to fit. Allowable values are `"lm"`, `"glm"`,
+#'  `"survreg"`, and `"coxph"`. The latter two require that the survival package
 #'  be installed.
-#' @param type The type of bootstrap confidence interval. Values of "student-t" and
-#' "percentile" are allowed.
+#' @param type The type of bootstrap confidence interval. Values of `"student-t"` and
+#' `"percentile"` are allowed.
 #' @param times A single integer for the number of bootstrap samples. If left
-#' NULL, 1,001 are used for t-intervals and 2,001 for percentile intervals.
+#' `NULL`, 1,001 are used for t-intervals and 2,001 for percentile intervals.
 #' @param alpha Level of significance.
 #' @param filter A logical expression used to remove rows from the final result, or `NULL` to keep all rows.
 #' @param keep_reps Should the individual parameter estimates for each bootstrap
 #' sample be retained?
-#' @param ... Options to pass to the model function (such as `family` for `glm()`).
+#' @param ... Options to pass to the model function (such as `family` for [stats::glm()]).
 #' @return A tibble with columns "term", ".lower", ".estimate", ".upper",
 #' ".alpha", and ".method". If `keep_reps = TRUE`, an additional list column
 #' called ".replicates" is also returned.
diff --git a/R/rsplit.R b/R/rsplit.R
index 744e9d9e..394145a4 100644
--- a/R/rsplit.R
+++ b/R/rsplit.R
@@ -66,12 +66,12 @@ 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?
-#' @param data Either "analysis" or "assessment" to specify which data are returned.
+#' @param data Either `"analysis"` or `"assessment"` to specify which data are returned.
 #' @param ... Not currently used.
 #' @examples
 #' library(dplyr)
diff --git a/R/tidy.R b/R/tidy.R
index acba8b33..39c73ba1 100644
--- a/R/tidy.R
+++ b/R/tidy.R
@@ -1,19 +1,19 @@
 #' 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
+#' @param x A `rset` or `rsplit` object
 #' @param unique_ind Should unique row identifiers be returned? For example,
 #'  if `FALSE` then bootstrapping results will include multiple rows in the
 #'  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.
diff --git a/man/add_resample_id.Rd b/man/add_resample_id.Rd
index a1d6450a..5b73deb1 100644
--- a/man/add_resample_id.Rd
+++ b/man/add_resample_id.Rd
@@ -7,7 +7,7 @@
 add_resample_id(.data, split, dots = FALSE)
 }
 \arguments{
-\item{.data}{A data frame}
+\item{.data}{A data frame.}
 
 \item{split}{A single \code{rset} object.}
 
diff --git a/man/as.data.frame.rsplit.Rd b/man/as.data.frame.rsplit.Rd
index 6a494c15..1ac6b7cb 100644
--- a/man/as.data.frame.rsplit.Rd
+++ b/man/as.data.frame.rsplit.Rd
@@ -31,15 +31,15 @@ assessment(x, ...)
 
 \item{optional}{A logical: should the column names of the data be checked for legality?}
 
-\item{data}{Either "analysis" or "assessment" to specify which data are returned.}
+\item{data}{Either \code{"analysis"} or \code{"assessment"} to specify which data are returned.}
 
 \item{...}{Not currently used.}
 }
 \description{
 The analysis or assessment code can be returned as a data
 frame (as dictated by the \code{data} argument) using
-\code{as.data.frame.rsplit}. \code{analysis} and
-\code{assessment} are shortcuts.
+\code{as.data.frame.rsplit()}. \code{analysis()} and
+\code{assessment()} are shortcuts.
 }
 \examples{
 library(dplyr)
diff --git a/man/bootstraps.Rd b/man/bootstraps.Rd
index 164f1835..f56a4d8a 100644
--- a/man/bootstraps.Rd
+++ b/man/bootstraps.Rd
@@ -33,7 +33,7 @@ of stratifying groups that are too small.}
 
 \item{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 \code{summary} function that require the apparent
+some estimators used by the \code{\link[=summary]{summary()}} function that require the apparent
 error rate.}
 
 \item{...}{These dots are for future extensions and must be empty.}
diff --git a/man/form_pred.Rd b/man/form_pred.Rd
index e80e43e9..189f50c0 100644
--- a/man/form_pred.Rd
+++ b/man/form_pred.Rd
@@ -16,7 +16,7 @@ object.}
 A character vector of names
 }
 \description{
-\code{all.vars} returns all variables used in a formula. This
+While \code{\link[=all.vars]{all.vars()}} returns all variables used in a formula, this
 function only returns the variables explicitly used on the
 right-hand side (i.e., it will not resolve dots unless the
 object is terms with a data set specified).
diff --git a/man/group_bootstraps.Rd b/man/group_bootstraps.Rd
index ad68eb6d..9a3c7878 100644
--- a/man/group_bootstraps.Rd
+++ b/man/group_bootstraps.Rd
@@ -25,7 +25,7 @@ assessment set within a fold.}
 
 \item{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 \code{summary} function that require the apparent
+some estimators used by the \code{\link[=summary]{summary()}} function that require the apparent
 error rate.}
 
 \item{...}{These dots are for future extensions and must be empty.}
diff --git a/man/labels.rset.Rd b/man/labels.rset.Rd
index 6aa2ca12..0f314296 100644
--- a/man/labels.rset.Rd
+++ b/man/labels.rset.Rd
@@ -10,7 +10,7 @@
 \method{labels}{vfold_cv}(object, make_factor = FALSE, ...)
 }
 \arguments{
-\item{object}{An \code{rset} object}
+\item{object}{An \code{rset} object.}
 
 \item{make_factor}{A logical for whether the results should be
 a character or a factor.}
@@ -22,8 +22,7 @@ A single character or factor vector.
 }
 \description{
 Produce a vector of resampling labels (e.g. "Fold1") from
-an \code{rset} object. Currently, \code{nested_cv}
-is not supported.
+an \code{rset} object. Currently, \code{\link[=nested_cv]{nested_cv()}} is not supported.
 }
 \examples{
 labels(vfold_cv(mtcars))
diff --git a/man/make_groups.Rd b/man/make_groups.Rd
index d2bdc62a..508d9c5e 100644
--- a/man/make_groups.Rd
+++ b/man/make_groups.Rd
@@ -49,6 +49,6 @@ Similarly, \code{\link[=group_mc_cv]{group_mc_cv()}} and its derivatives don't a
 only one) assessment set, but rather allow each observation to be in an
 assessment set zero-or-more times. As a result, those functions don't have
 a \code{balance} argument, and under the hood always specify \code{balance = "prop"}
-when they call \code{\link[=make_groups]{make_groups()}}.
+when they call \code{make_groups()}.
 }
 \keyword{internal}
diff --git a/man/make_strata.Rd b/man/make_strata.Rd
index c2b7b434..9d9c50bc 100644
--- a/man/make_strata.Rd
+++ b/man/make_strata.Rd
@@ -64,7 +64,6 @@ x3 <- factor(x2)
 table(x3)
 table(make_strata(x3))
 
-# `oilType` data from 
 x4 <- rep(LETTERS[1:7], c(37, 26, 3, 7, 11, 10, 2))
 table(x4)
 table(make_strata(x4))
diff --git a/man/nested_cv.Rd b/man/nested_cv.Rd
index 217ef2db..84f67acb 100644
--- a/man/nested_cv.Rd
+++ b/man/nested_cv.Rd
@@ -25,7 +25,7 @@ and a column of nested tibbles called \code{inner_resamples} with the
 additional resamples.
 }
 \description{
-\code{nested_cv} can be used to take the results of one resampling procedure
+\code{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.
 }
diff --git a/man/permutations.Rd b/man/permutations.Rd
index c2773bed..54c14324 100644
--- a/man/permutations.Rd
+++ b/man/permutations.Rd
@@ -33,7 +33,7 @@ A permutation sample is the same size as the original data set and is made
 by permuting/shuffling one or more columns. This results in analysis
 samples where some columns are in their original order and some columns
 are permuted to a random order. Unlike other sampling functions in
-rsample, there is no assessment set and calling \code{assessment()} on a
+rsample, there is no assessment set and calling \code{\link[=assessment]{assessment()}} on a
 permutation split will throw an error.
 }
 \details{
diff --git a/man/reg_intervals.Rd b/man/reg_intervals.Rd
index cf3b04a2..afc386e9 100644
--- a/man/reg_intervals.Rd
+++ b/man/reg_intervals.Rd
@@ -21,15 +21,15 @@ reg_intervals(
 
 \item{data}{A data frame.}
 
-\item{model_fn}{The model to fit. Allowable values are "lm", "glm",
-"survreg", and "coxph". The latter two require that the \code{survival} package
+\item{model_fn}{The model to fit. Allowable values are \code{"lm"}, \code{"glm"},
+\code{"survreg"}, and \code{"coxph"}. The latter two require that the survival package
 be installed.}
 
-\item{type}{The type of bootstrap confidence interval. Values of "student-t" and
-"percentile" are allowed.}
+\item{type}{The type of bootstrap confidence interval. Values of \code{"student-t"} and
+\code{"percentile"} are allowed.}
 
 \item{times}{A single integer for the number of bootstrap samples. If left
-NULL, 1,001 are used for t-intervals and 2,001 for percentile intervals.}
+\code{NULL}, 1,001 are used for t-intervals and 2,001 for percentile intervals.}
 
 \item{alpha}{Level of significance.}
 
@@ -38,7 +38,7 @@ NULL, 1,001 are used for t-intervals and 2,001 for percentile intervals.}
 \item{keep_reps}{Should the individual parameter estimates for each bootstrap
 sample be retained?}
 
-\item{...}{Options to pass to the model function (such as \code{family} for \code{glm()}).}
+\item{...}{Options to pass to the model function (such as \code{family} for \code{\link[stats:glm]{stats::glm()}}).}
 }
 \value{
 A tibble with columns "term", ".lower", ".estimate", ".upper",
diff --git a/man/rsample2caret.Rd b/man/rsample2caret.Rd
index ca0605f1..e96fc98b 100644
--- a/man/rsample2caret.Rd
+++ b/man/rsample2caret.Rd
@@ -11,20 +11,20 @@ caret2rsample(ctrl, data = NULL)
 }
 \arguments{
 \item{object}{An \code{rset} object. Currently,
-\code{nested_cv} is not supported.}
+\code{\link[=nested_cv]{nested_cv()}} is not supported.}
 
 \item{data}{The data that was originally used to produce the
 \code{ctrl} object.}
 
-\item{ctrl}{An object produced by \code{trainControl} that has
+\item{ctrl}{An object produced by \code{caret::trainControl()} that has
 had the \code{index} and \code{indexOut} elements populated by
 integers. One method of getting this is to extract the
 \code{control} objects from an object produced by \code{train}.}
 }
 \value{
-\code{rsample2caret} returns a list that mimics the
+\code{rsample2caret()} returns a list that mimics the
 \code{index} and \code{indexOut} elements of a
-\code{trainControl} object. \code{caret2rsample} returns an
+\code{trainControl} object. \code{caret2rsample()} returns an
 \code{rset} object of the appropriate class.
 }
 \description{
diff --git a/man/tidy.rsplit.Rd b/man/tidy.rsplit.Rd
index 01c419ec..3f2bec7f 100644
--- a/man/tidy.rsplit.Rd
+++ b/man/tidy.rsplit.Rd
@@ -16,7 +16,7 @@
 \method{tidy}{nested_cv}(x, unique_ind = TRUE, ...)
 }
 \arguments{
-\item{x}{A  \code{rset} or  \code{rsplit} object}
+\item{x}{A \code{rset} or \code{rsplit} object}
 
 \item{unique_ind}{Should unique row identifiers be returned? For example,
 if \code{FALSE} then bootstrapping results will include multiple rows in the
@@ -26,14 +26,14 @@ sample for the same row in the original data.}
 }
 \value{
 A tibble with columns \code{Row} and \code{Data}. The latter has possible
-values "Analysis" or "Assessment". For \code{rset} inputs, identification columns
-are also returned but their names and values depend on the type of
-resampling. \code{vfold_cv} contains a column "Fold" and, if repeats are used,
-another called "Repeats". \code{bootstraps} and \code{mc_cv} use the column
+values "Analysis" or "Assessment". For \code{rset} inputs, identification
+columns are also returned but their names and values depend on the type of
+resampling. For \code{\link[=vfold_cv]{vfold_cv()}}, contains a column "Fold" and, if repeats are
+used, another called "Repeats". \code{\link[=bootstraps]{bootstraps()}} and \code{\link[=mc_cv]{mc_cv()}} use the column
 "Resample".
 }
 \description{
-The \code{tidy} function from the \pkg{broom} package can be used on \code{rset} and
+The \code{tidy()} function from the \pkg{broom} package can be used on \code{rset} and
 \code{rsplit} objects to generate tibbles with which rows are in the analysis and
 assessment sets.
 }
diff --git a/vignettes/Working_with_rsets.Rmd b/vignettes/Working_with_rsets.Rmd
index 4d97c71b..a9146c37 100644
--- a/vignettes/Working_with_rsets.Rmd
+++ b/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)
@@ -182,8 +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}
 glm_coefs <- function(splits, ...) {