fix: links

R/AutoFSelector.R

@@ -161,7 +161,7 @@ AutoFSelector = R6Class("AutoFSelector",
     #' @param recursive (`integer(1)`)\cr
     #'   Depth of recursion for multiple nested objects.
     #'
-    #' @return [Learner].
+    #' @return [mlr3::Learner].
     base_learner = function(recursive = Inf) {
       if (recursive == 0L) self$learner else self$learner$base_learner(recursive - 1L)
     },

R/CallbackBatchFSelect.R

@@ -76,7 +76,7 @@ CallbackBatchFSelect = R6Class("CallbackBatchFSelect",
 #' This context can be used to customize the evaluation of a batch of feature sets.
 #' Changes to the state of callback are lost after the evaluation of a batch and changes to the fselect instance or the fselector are not possible.
 #' Persistent data should be written to the archive via `$aggregated_performance` (see [ContextBatchFSelect]).
-#' The other stages access [ContextBatch].
+#' The other stages access [bbotk::ContextBatch].
 #' This context can be used to modify the fselect instance, archive, fselector and final result.
 #' There are two different contexts because the evaluation can be parallelized i.e. multiple instances of [ContextBatchFSelect] exists on different workers at the same time.
 #'

R/ensemble_fselect.R

@@ -20,7 +20,7 @@
 #' @param init_resampling ([mlr3::Resampling])\cr
 #'  The initial resampling strategy of the data, from which each train set
 #'  will be passed on to the learners.
-#'  Can only be [mlr_resamplings_subsampling] or [mlr_resamplings_bootstrap].
+#'  Can only be [mlr3::ResamplingSubsampling] or [mlr3::ResamplingBootstrap].
 #' @param inner_resampling ([mlr3::Resampling])\cr
 #'  The inner resampling strategy used by the [FSelector].
 #' @param store_benchmark_result (`logical(1)`)\cr

R/fselect.R

@@ -9,11 +9,11 @@
 #' The [ArchiveBatchFSelect] (`$archive`) stores all evaluated hyperparameter configurations and performance scores.
 #'
 #' @details
-#' The [mlr3::Task], [mlr3::Learner], [mlr3::Resampling], [mlr3::Measure] and [Terminator] are used to construct a [FSelectInstanceBatchSingleCrit].
-#' If multiple performance [Measures][Measure] are supplied, a [FSelectInstanceBatchMultiCrit] is created.
-#' The parameter `term_evals` and `term_time` are shortcuts to create a [Terminator].
-#' If both parameters are passed, a [TerminatorCombo] is constructed.
-#' For other [Terminators][Terminator], pass one with `terminator`.
+#' The [mlr3::Task], [mlr3::Learner], [mlr3::Resampling], [mlr3::Measure] and [bbotk::Terminator] are used to construct a [FSelectInstanceBatchSingleCrit].
+#' If multiple performance [Measures][mlr3::Measure] are supplied, a [FSelectInstanceBatchMultiCrit] is created.
+#' The parameter `term_evals` and `term_time` are shortcuts to create a [bbotk::Terminator].
+#' If both parameters are passed, a [bbotk::TerminatorCombo] is constructed.
+#' For other [Terminators][bbotk::Terminator], pass one with `terminator`.
 #' If no termination criterion is needed, set `term_evals`, `term_time` and `terminator` to `NULL`.
 #'
 #' @inheritSection FSelectInstanceBatchSingleCrit Resources

R/sugar.R

@@ -6,8 +6,8 @@
 #'
 #' * `fs()` for a [FSelector] from [mlr_fselectors].
 #' * `fss()` for a list of a [FSelector] from [mlr_fselectors].
-#' * `trm()` for a [Terminator] from [mlr_terminators].
-#' * `trms()` for a list of [Terminators][Terminator] from [mlr_terminators].
+#' * `trm()` for a [bbotk::Terminator] from [mlr_terminators].
+#' * `trms()` for a list of [Terminators][bbotk::Terminator] from [mlr_terminators].
 #'
 #' @inheritParams mlr3::mlr_sugar
 #' @return [R6::R6Class] object of the respective type, or a list of [R6::R6Class] objects for the plural versions.

man-roxygen/param_terminator.R

@@ -1,2 +1,2 @@
-#' @param terminator ([Terminator])\cr
+#' @param terminator ([bbotk::Terminator])\cr
 #'   Stop criterion of the feature selection.