Title: Hyperparameter Optimization for 'mlr3'
person("Marc", "Becker", , "marcbecker@posteo.de", role = c("cre", "aut"),
person("Daniel", "Schalk", , "daniel.schalk@stat.uni-muenchen.de", role = "aut",
Description: 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

Suggests: bibtex, GenSA, mlr3pipelines, nloptr, rpart, testthat
Collate: 'AutoTuner.R' 'ObjectiveTuning.R' 'mlr_tuners.R' 'Tuner.R'
'assertions.R' 'helper.R' 'reexport.R' 'sugar.R' 'zzz.R'

Suggests: GenSA, mlr3pipelines, nloptr, rpart, testthat
'assertions.R' 'bibentries.R' 'helper.R' 'reexport.R' 'sugar.R'

Imports: bbotk (>= 0.2.2), checkmate (>= 2.0.0), data.table, lgr, mlr3
Suggests: adagio, GenSA, mlr3pipelines, nloptr, rpart, testthat (>=

Imports: bbotk (>= 0.3.0), checkmate (>= 2.0.0), data.table, lgr,

Imports: bbotk (>= 0.4.0), checkmate (>= 2.0.0), data.table, digest,

Title: Search Spaces for Hyperparameter Tuning
Description: Collection of search spaces for hyperparameter tuning.
Includes various search spaces that can be directly applied on an
`mlr3` learner. Additionally, meta information about the search space
Imports: checkmate (>= 2.0.0), data.table (>= 1.14.0), mlr3 (>=
Collate: 'mlr_tuning_spaces.R' 'TuningSpace.R' 'bibentries.R' 'sugar.R'

Title: Easily Install and Load the 'mlr3' package family
Description: The 'mlr3' package family is a set of packages for
machine-learning purposes built in a modular fashion. This wrapper
package is aimed to simplify the installation and loading of the core
'mlr3' packages. Get more information about the 'mlr3' project at
Depends: mlr3, mlr3db, mlr3filters, mlr3learners, mlr3pipelines,
Author: Patrick Schratz [cre, aut] (<https://orcid.org/0000-0003-0748-6624>),