Changelog

Option to control store_benchmark_result, store_models and check_values in AutoTuner. store_tuning_instance must be set as a parameter during initialization.

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 behaviour 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).

mlr3tuning 0.3.0.9000 Unreleased