Imports: bbotk (>= 0.4.0), checkmate (>= 2.0.0), data.table, lgr,
mlr3misc (>= 0.9.4), mlr3pipelines (>= 0.3.0), paradox (>=

Imports: bbotk (>= 0.5.0), checkmate (>= 2.0.0), data.table, lgr,

Imports: bbotk (>= 0.5.2), checkmate (>= 2.0.0), data.table, lgr,

'fselect_nested.R' 'helper.R' 'reexports.R' 'sugar.R' 'zzz.R'

person("Marc", "Becker", , "marcbecker@posteo.de", role = c("aut", "cre"),
Description: Feature selection package of the mlr3 ecosystem. It selects
the optimal feature set for any mlr3 learner. The package works with
several optimization algorithms e.g. Random Search, Recursive Feature
Elimination, and Genetic Search. Moreover, it can automatically
optimize learners and estimate the performance of optimized feature

Description: Implements hyperband method for hyperparameter
tuning.  Various termination criteria can be set and combined. The
class 'AutoTuner' provides a convenient way to perform nested
resampling in combination with 'mlr3'. The hyperband algorithm was
proposed by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin
Rostamizadeh and Ameet Talwalkar (2018) <arXiv:1603.06560>.
Imports: bbotk (>= 0.2.0), checkmate (>= 1.9.4), data.table, lgr, mlr3
Suggests: emoa, mlr3learners, mlr3pipelines, rpart, testthat, xgboost
Collate: 'TunerHyperband.R' 'nds_selection.R' 'bibentries.R' 'zzz.R'

Imports: bbotk (>= 0.3.0), checkmate (>= 1.9.4), data.table, lgr, mlr3
Suggests: emoa, mlr3learners, mlr3pipelines, rpart, testthat (>=

Imports: bbotk (>= 0.5.0), checkmate (>= 1.9.4), data.table, lgr, mlr3

Suggests: emoa, mlr3learners (>= 0.5.2), mlr3pipelines, rpart, testthat

Imports: bbotk (>= 0.5.2), checkmate (>= 1.9.4), data.table, lgr, mlr3

Imports: bbotk (>= 0.7.0), checkmate (>= 1.9.4), data.table, lgr, mlr3

person("Sebastian", "Gruber", , "gruber_sebastian@t-online.de", role = "aut",
person("Julia", "Moosbauer", , "ju.moosbauer@googlemail.com", role = "aut",
Description: Implements hyperband method for hyperparameter tuning.
Various termination criteria can be set and combined. The class
'AutoTuner' provides a convenient way to perform nested resampling in
combination with 'mlr3'. The hyperband algorithm was proposed by Lisha
Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh and Ameet