mlr3tuning 0.14.0

feat: Add option evaluate_default to evaluate learners with hyperparameters set to their default values.
refactor: From now on, the default of smooth is FALSE for TunerGenSA.

mlr3tuning 0.13.1

feat: Allow to pass Tuner objects as method in tune() and auto_tuner().
docs: Link Tuner to help page of bbotk::Optimizer.
feat: Tuner objects have the optional field $label now.
feat: as.data.table() functions for objects of class Dictionary have been extended with additional columns.

feat: as_search_space() function to create search spaces from Learner and ParamSet objects. Allow to pass TuningSpace objects as search_space in TuningInstanceSingleCrit and TuningInstanceMultiCrit.
feat: The mlr3::HotstartStack can now be removed after tuning with the keep_hotstart_stack flag.
feat: The Archive stores errors and warnings of the learners.
feat: When no measure is provided, the default measure is used in auto_tuner() and tune_nested().

fix: $assign_result() method in TuningInstanceSingleCrit when search space is empty.
feat: Default measure is used when no measure is supplied to TuningInstanceSingleCrit.

Adds AutoTuner$base_learner() method to extract the base learner from nested learner objects.
tune() supports multi-criteria tuning.
Allows empty search space.
Adds TunerIrace from irace package.
extract_inner_tuning_archives() helper function to extract inner tuning archives.
Removes ArchiveTuning$extended_archive() method. The mlr3::ResampleResults are joined automatically by as.data.table.TuningArchive() and extract_inner_tuning_archives().

Adds tune(), auto_tuner() and tune_nested() sugar functions.
TuningInstanceSingleCrit, TuningInstanceMultiCrit and AutoTuner can be initialized with store_benchmark_result = FALSE and store_models = TRUE to allow measures to access the models.
Prettier printing methods.

Fix TuningInstance*$assign_result() errors with required parameter bug.
Shortcuts to access $learner(), $learners(), $learner_param_vals(), $predictions() and $resample_result() from benchmark result in archive.
extract_inner_tuning_results() helper function to extract inner tuning results.

Adds TunerCmaes from adagio package.
Fix predict_type in AutoTuner.
Support to set TuneToken in Learner$param_set and create a search space from it.
The order of the parameters in TuningInstanceSingleCrit and TuningInstanceSingleCrit changed.

Option to control store_benchmark_result, store_models and check_values in AutoTuner. store_tuning_instance must be set as a parameter during initialization.
Fixes check_values flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit.
Removed dependency on orphaned package bibtex.

Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc.
Archive is ArchiveTuning now which stores the benchmark result in $benchmark_result. This change removed the resample results from the archive but they can be still accessed via the benchmark result.
Warning message if external package for tuning is not installed.
To retrieve the inner tuning results in nested resampling, as.data.table(rr)$learner[[1]]$tuning_result must be used now.

TuningInstance is now TuningInstanceSingleCrit. TuningInstanceMultiCrit is still available for multi-criteria tuning.
Terminators are now accessible by trm() and trms() instead of term() and terms().
Storing of resample results is optional now by using the store_resample_result flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit
TunerNLoptr adds non-linear optimization from the nloptr package.
Logging is controlled by the bbotk logger now.
Proposed points and performance values can be checked for validity by activating the check_values flag in TuningInstanceSingleCrit and TuningInstanceMultiCrit.

mlr3tuning now depends on the bbotk package for basic tuning objects. Terminator classes now live in bbotk. As a consequence ObjectiveTuning inherits from bbotk::Objective, TuningInstance from bbotk::OptimInstance and Tuner from bbotk::Optimizer
TuningInstance$param_set becomes TuningInstance$search_space to avoid confusion as the param_set usually contains the parameters that change the behavior of an object.
Tuning is triggered by $optimize() instead of $tune()

Fixed a bug in AutoTuner where a $clone() was missing. Tuning results are unaffected, only stored models contained wrong hyperparameter values (#223).
Improved output log (#218).