Various docs changes

R/PipeOpClassWeights.R

@@ -14,7 +14,7 @@
 #' must be adjusted for this [`PipeOp`] to be useful.
 #'
 #' Note this only sets the `"weights_learner"` column.
-#' It therefore influences the behaviour of subsequent `Learner`s, but does not influence resampling or evaluation metric weights.
+#' It therefore influences the behaviour of subsequent [`Learner`][mlr3::Learner]s, but does not influence resampling or evaluation metric weights.
 #'
 #' @section Construction:
 #' ```

R/PipeOpClassifAvg.R

@@ -20,8 +20,6 @@
 #' equal weights for each prediction.
 #' Defaults to equal weights for each model.
 #'
-#' If `
-#'
 #' @section Construction:
 #' ```
 #' PipeOpClassifAvg$new(innum = 0, collect_multiplicity = FALSE, id = "classifavg", param_vals = list())

R/PipeOpEncodeImpact.R

@@ -43,6 +43,7 @@
 #'   this feature level on each *outcome level*.
 #'
 #' @section Parameters:
+#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
 #' * `smoothing`  :: `numeric(1)` \cr
 #'   A finite positive value used for smoothing. Mostly relevant for [classification Tasks][mlr3::TaskClassif] if
 #'   a factor does not coincide with a target factor level (and would otherwise give an infinite logit value).
@@ -54,7 +55,7 @@
 #' Uses Laplace smoothing, mostly to avoid infinite values for [classification Task][mlr3::TaskClassif].
 #'
 #' @section Methods:
-#' Only methods inherited [`PipeOpTaskPreproc`]/[`PipeOp`].
+#' Only methods inherited [`PipeOpTaskPreprocSimple`][`PipeOpTaskPreproc`]/[`PipeOp`].
 #'
 #' @examples
 #' library("mlr3")

R/PipeOpEncodeLmer.R

@@ -55,11 +55,11 @@
 #'   List of coefficients learned via `glmer`
 #'
 #' @section Parameters:
+#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
 #' * `fast_optim`  :: `logical(1)` \cr
+#'   If `fast_optim` is `TRUE` (default), a faster (up to 50 percent) optimizer from the `nloptr` package is used when
+#'   fitting the lmer models. This uses additional stopping criteria which can give suboptimal results.
 #'   Initialized to `TRUE`.
-#'   If "fast_optim" is `TRUE` (default), a faster (up to 50 percent)
-#'   optimizer from the nloptr package is used when fitting the lmer models.
-#'   This uses additional stopping criteria which can give suboptimal results.
 #'
 #' @section Internals:
 #' Uses the [`lme4::glmer`]. This is relatively inefficient for features with a large number of levels.

R/PipeOpTaskPreproc.R

@@ -39,7 +39,6 @@
 #' PipeOpTaskPreproc$new(id, param_set = ps(), param_vals = list(), can_subset_cols = TRUE,
 #'   packages = character(0), task_type = "Task", tags = NULL, feature_types = mlr_reflections$task_feature_types)
 #' ```
-#'
 #' * `id` :: `character(1)`\cr
 #'   Identifier of resulting object. See `$id` slot of [`PipeOp`].
 #' * `param_set` :: [`ParamSet`][paradox::ParamSet]\cr
@@ -51,14 +50,14 @@
 #'   Whether the `affect_columns` parameter should be added which lets the user limit the columns that are
 #'   modified by the [`PipeOpTaskPreproc`]. This should generally be `FALSE` if the operation adds or removes
 #'   rows from the [`Task`][mlr3::Task], and `TRUE` otherwise. Default is `TRUE`.
-#' * packages :: `character`\cr
+#' * `packages` :: `character`\cr
 #'   Set of all required packages for the [`PipeOp`]'s `private$.train()` and `private$.predict()` methods. See `$packages` slot.
 #'   Default is `character(0)`.
 #' * `task_type` :: `character(1)`\cr
 #'   The class of [`Task`][mlr3::Task] that should be accepted as input and will be returned as output. This
 #'   should generally be a `character(1)` identifying a type of [`Task`][mlr3::Task], e.g. `"Task"`, `"TaskClassif"` or
 #'   `"TaskRegr"` (or another subclass introduced by other packages). Default is `"Task"`.
-#' * tags :: `character` | `NULL`\cr
+#' * `tags` :: `character` | `NULL`\cr
 #'   Tags of the resulting `PipeOp`. This is added to the tag `"data transform"`. Default `NULL`.
 #'* `feature_types` :: `character`\cr
 #'   Feature types affected by the `PipeOp`. See `private$.select_cols()` for more information.
@@ -324,8 +323,10 @@ PipeOpTaskPreproc = R6Class("PipeOpTaskPreproc",
 #'
 #' @section Construction:
 #' ```
-#' PipeOpTaskPreprocSimple$new(id, param_set = ps(), param_vals = list(), can_subset_cols = TRUE, packages = character(0), task_type = "Task")
+#' PipeOpTaskPreprocSimple$new(id, param_set = ps(), param_vals = list(), can_subset_cols = TRUE,
+#'   packages = character(0), task_type = "Task", tags = NULL, feature_types = mlr_reflections$task_feature_types)
 #' ```
+#'
 #' (Construction is identical to [`PipeOpTaskPreproc`].)
 #'
 #' * `id` :: `character(1)`\cr
@@ -339,13 +340,18 @@ PipeOpTaskPreproc = R6Class("PipeOpTaskPreproc",
 #'   Whether the `affect_columns` parameter should be added which lets the user limit the columns that are
 #'   modified by the [`PipeOpTaskPreprocSimple`]. This should generally be `FALSE` if the operation adds or removes
 #'   rows from the [`Task`][mlr3::Task], and `TRUE` otherwise. Default is `TRUE`.
-#' * packages :: `character`\cr
+#' * `packages` :: `character`\cr
 #'   Set of all required packages for the [`PipeOp`]'s `private$.train()` and `private$.predict()` methods. See `$packages` slot.
 #'   Default is `character(0)`.
 #' * `task_type` :: `character(1)`\cr
 #'   The class of [`Task`][mlr3::Task] that should be accepted as input and will be returned as output. This
 #'   should generally be a `character(1)` identifying a type of [`Task`][mlr3::Task], e.g. `"Task"`, `"TaskClassif"` or
 #'   `"TaskRegr"` (or another subclass introduced by other packages). Default is `"Task"`.
+#' * `tags` :: `character` | `NULL`\cr
+#'   Tags of the resulting `PipeOp`. This is added to the tag `"data transform"`. Default `NULL`.
+#'* `feature_types` :: `character`\cr
+#'   Feature types affected by the `PipeOp`. See `private$.select_cols()` for more information.
+#'   Defaults to all available feature types.
 #'
 #' @section Input and Output Channels:
 #' Input and output channels are inherited from [`PipeOpTaskPreproc`].