mlr3fselect 0.7.1

mlr3fselect 0.7.0

feat: Allow to pass FSelector objects as method in fselect() and auto_fselector().
feat: Added $label to FSelectors.
docs: New examples with fselect() function.
feat: $help() method which opens manual page of a FSelector.
feat: Added a as.data.table.DictionaryFSelector function.
feat: Added min_features parameter to FSelectorSequential.

Adds AutoFSelector$base_learner() method to extract the base learner from nested learner objects.
Adds fselect(), auto_fselector() and fselect_nested() sugar functions.
Adds extract_inner_fselect_results() and extract_inner_fselect_archives() helper function to extract inner feature selection results and archives.

FSelectorRFE stores importance values of each evaluated feature set in archive.
ArchiveFSelect$data is a public field now.

Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
FSelectorRFE supports fraction of features to retain in each iteration (feature_fraction), number of features to remove in each iteration (feature_number) and vector of number of features to retain in each iteration (subset_sizes).
AutoFSelect is renamed to AutoFSelector.
To retrieve the inner feature selection results in nested resampling, as.data.table(rr)$learner[[1]]$fselect_result must be used now.
Option to control store_benchmark_result, store_models and check_values in AutoFSelector. store_fselect_instance must be set as a parameter during initialization.
Adds FSelectorGeneticSearch.
Fixes check_values flag in FSelectInstanceSingleCrit and FSelectInstanceMultiCrit.
Removed dependency on orphaned package bibtex.
PipeOpSelect is internally used for task subsetting.

Archive is ArchiveFSelect now which stores the benchmark result in $benchmark_result. This change removed the resample results from the archive but they can be still accessed via the benchmark result.