adagio, GenSA, irace, mlr3pipelines, nloptr, rpart, testthat
(>= 3.0.0),

'ArchiveTuning.R' 'AutoTuner.R' 'ObjectiveTuning.R'
'mlr_tuners.R' 'Tuner.R' 'TunerCmaes.R' 'TunerDesignPoints.R'
'TunerFromOptimizer.R' 'TunerGenSA.R' 'TunerGridSearch.R'
'TunerIrace.R' 'TunerNLoptr.R' 'TunerRandomSearch.R'
'TuningInstanceSingleCrit.R' 'TuningInstanceMulticrit.R'
'as_search_space.R' 'assertions.R' 'auto_tuner.R'
'bibentries.R' 'extract_inner_tuning_archives.R'
'extract_inner_tuning_results.R' 'helper.R' 'reexport.R'
'sugar.R' 'tune.R' 'tune_nested.R' 'zzz.R'

adagio, GenSA, irace, mlr3learners (>= 0.5.5), mlr3pipelines,
nloptr, rpart, testthat (>= 3.0.0), xgboost
'ArchiveTuning.R' 'AutoTuner.R' 'CallbackTuning.R'
'ContextEval.R' 'ObjectiveTuning.R' 'mlr_tuners.R' 'Tuner.R'
'TunerCmaes.R' 'TunerDesignPoints.R' 'TunerFromOptimizer.R'
'TunerGenSA.R' 'TunerGridSearch.R' 'TunerIrace.R'
'TunerNLoptr.R' 'TunerRandomSearch.R'
'TuningInstanceSingleCrit.R' 'TuningInstanceMulticrit.R'
'as_search_space.R' 'assertions.R' 'auto_tuner.R'
'bibentries.R' 'extract_inner_tuning_archives.R'
'extract_inner_tuning_results.R' 'helper.R' 'mlr_callbacks.R'
'reexport.R' 'sugar.R' 'tune.R' 'tune_nested.R' 'zzz.R'

c(
person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut"),
comment = c(ORCID = "0000-0002-8115-0400")),
person("Michel", "Lang", , "michellang@gmail.com", role = "aut",
comment = c(ORCID = "0000-0001-9754-0393")),
person("Jakob", "Richter", , "jakob1richter@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-4481-5554")),
person("Bernd", "Bischl", , "bernd_bischl@gmx.net", role = "aut",
comment = c(ORCID = "0000-0001-6002-6980")),
person("Daniel", "Schalk", , "daniel.schalk@stat.uni-muenchen.de", role = "aut",
comment = c(ORCID = "0000-0003-0950-1947"))
)
Hyperparameter optimization package of the mlr3 ecosystem. It
features highly configurable search spaces via the paradox package and
finds optimal hyperparameter configurations for any mlr3 learner.
mlr3tuning works with several optimization algorithms e.g. Random
Search, Iterated Racing, Bayesian Optimization (in mlr3mbo) and
Hyperband (in mlr3hyperband). Moreover, it can automatically optimize
learners and estimate the performance of optimized models with nested
resampling.