docs standardization

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.