man file updates with value tags

R/C5_rules.R

@@ -46,6 +46,7 @@
 #' turns this feature off (although it can be re-enabled using
 #' [C50::C5.0Control()]).
 #'
+#' @return An updated `parsnip` model specification.
 #' @seealso [parsnip::fit()], [parsnip::fit_xy()], [C50::C5.0()],
 #' [C50::C5.0Control()]
 #' @references Quinlan R (1993). _C4.5: Programs for Machine Learning_. Morgan
@@ -295,6 +296,10 @@ c5_pred <- function(object, new_data, trials = object$fit$trials["Actual"], ...)
 #' @param type A single character value or `NULL`. Possible values
 #' are class" and "prob".
 #' @param ... Not currently used.
+#' @return A tibble with one row for each row of `new_data`. Multiple
+#' predictions are contained in a list column called `.pred`. That column has
+#' the standard `parsnip` prediction column names as well as the column with
+#' the tuning parameter values.
 #' @details
 #' For C5.0 rule-based models, the model fit may contain less boosting
 #' iterations than the number requested. Printing the object will show how many

R/cubist.R

@@ -63,6 +63,7 @@
 #'
 #' The only available engine is `"Cubist"`.
 #'
+#' @return An updated `parsnip` model specification.
 #' @seealso [parsnip::fit()], [parsnip::fit_xy()], [Cubist::cubist()],
 #' [Cubist::cubistControl()]
 #' @references Quinlan R (1992). "Learning with Continuous Classes." Proceedings
@@ -404,7 +405,7 @@ tunable.cubist_rules <- function(x, ...) {
  )
 }
 
-#' [multi_predict()] methods for rule-based models
+#' `multi_predict()` methods for rule-based models
 #' @rdname multi_predict
 #' @export
 #' @param neighbors An numeric vector of neighbors values between zero and nine.

R/rule_fit.R

@@ -52,6 +52,7 @@
 #'
 #' The only available engine is `"xrf"`.
 #'
+#' @return An updated `parsnip` model specification.
 #' @seealso [parsnip::fit()], [parsnip::fit_xy()], [xrf::xrf.formula()]
 #' @references Friedman, J. H., and Popescu, B. E. (2008). "Predictive learning
 #' via rule ensembles." _The Annals ofApplied Statistics_, 2(3), 916-954.
@@ -492,6 +493,8 @@ tunable.rule_fit <- function(x, ...) {
 #' Proportion of Randomly Selected Predictors
 #'
 #' @inheritParams committees
+#' @return A `dials` with classes "quant_param" and "param". The `range` element
+#' of the object is always converted to a list with elements "lower" and "upper".
 #' @export
 mtry_prop <- function(range = c(0.1, 1), trans = NULL) {
  dials::new_quant_param(