0.6.0), mlr3cluster, mlr3filters, mlr3learners, mlr3tuning (>=
ranger, rpart, stats, testthat (>= 3.0.0), vdiffr (>= 1.0.2),

Title: Composable Preprocessing Operators and Pipelines for Machine
Description: Toolset that enriches 'mlr' with a diverse set of preprocessing
operators. Composable Preprocessing Operators ("CPO"s) are first-class
R objects that can be applied to data.frames and 'mlr' "Task"s to modify
data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
learning algorithms, and can be composed to form preprocessing pipelines.
person("Martin", "Binder", email = "ma.binder@campus.lmu.de", role = c("aut", "cre")),
person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role = "ctb"),
person("Michel", "Lang", email = "michellang@gmail.com", role = "ctb"),
person("Lars", "Kotthoff", email = "larsko@cs.ubc.ca", role = "ctb"))
Imports: BBmisc (>= 1.11), stringi, checkmate (>= 1.8.3), methods,
Suggests: party, rpart, mlbench, knitr, rmarkdown, testthat, mRMRe,
care, randomForestSRC, randomForest, ranger (>= 0.8.0), Rfast,
FSelector, e1071, DiscriMiner, digest, lintr, Hmisc, fastICA

person("Martin", "Binder", email = "developer.mb706@doublecaret.com", role = c("aut", "cre")),
FSelector, e1071, DiscriMiner, digest, lintr, Hmisc, fastICA,

Suggests: care, party, rpart, mlbench, knitr, rmarkdown, testthat,
mRMRe, praznik, randomForestSRC, randomForest, ranger (>=
0.8.0), Rfast, FSelector, FSelectorRcpp, e1071, DiscriMiner,

0.8.0), Rfast, FSelector, FSelectorRcpp, e1071, digest, lintr,

0.8.0), Rfast, FSelector, FSelectorRcpp, e1071, FNN, digest,

mRMRe, digest, praznik, randomForestSRC, randomForest, ranger
(>= 0.8.0), Rfast, FSelector, FSelectorRcpp, e1071, FNN, lintr,

Title: A Toolbox for Model-Based Optimization of Expensive Black-Box
Description: Flexible and comprehensive R toolbox for model-based optimization
('MBO'), also known as Bayesian optimization. It is designed for both single-
and multi-objective optimization with mixed continuous, categorical and
conditional parameters. The machine learning toolbox 'mlr' provide dozens
of regression learners to model the performance of the target algorithm with
respect to the parameter settings. It provides many different infill criteria
to guide the search process. Additional features include multipoint batch
proposal, parallel execution as well as visualization and sophisticated
logging mechanisms, which is especially useful for teaching and understanding
of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
single components can be easily replaced or adapted by the user for specific use
person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role = c("aut")),
person("Jakob", "Bossek", email = "jakob.bossek@tu-dortmund.de", role = "aut"),
person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "aut"))
Depends: mlr (>= 2.10), ParamHelpers (>= 1.10), smoof (>= 1.4)
Imports: backports, BBmisc (>= 1.11), checkmate (>= 1.8.2), data.table,
Suggests: akima, cmaesr (>= 1.0.3), ggplot2, RColorBrewer, DiceKriging,
DiceOptim, earth, emoa, GGally, gridExtra, kernlab, kknn,
knitr, mco, nnet, party, randomForest, rmarkdown, rpart,