care, party, rpart, mlbench, knitr, rmarkdown, testthat,
mRMRe, digest, praznik, randomForestSRC, randomForest, ranger
(>= 0.8.0), Rfast, FSelector, FSelectorRcpp, e1071, FNN, lintr,
Hmisc, fastICA, rex

A Toolbox for Model-Based Optimization of Expensive Black-Box
Functions
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
cases.
c(
person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role = c("aut")),
person("Jakob", "Bossek", email = "jakob.bossek@tu-dortmund.de", role = "aut"),
person("Jakob", "Richter", email = "code@jakob-r.de", role = c("aut", "cre")),
person("Daniel", "Horn", email = "daniel.horn@tu-dortmund.de", role = "aut"),
person("Michel", "Lang", email = "michellang@gmail.com", role = "aut"),
person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "aut"))
backports, BBmisc (>= 1.11), checkmate (>= 1.8.2), data.table,
lhs, parallelMap (>= 1.3)
akima, cmaesr (>= 1.0.3), ggplot2, RColorBrewer, DiceKriging,
DiceOptim, earth, emoa, GGally, gridExtra, kernlab, kknn,
knitr, mco, nnet, party, randomForest, rmarkdown, rpart,
testthat, eaf, covr
Bernd Bischl [aut],
Jakob Bossek [aut],
Jakob Richter [aut, cre],
Daniel Horn [aut],
Michel Lang [aut],
Janek Thomas [aut]

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 multi-point 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
cases.
backports (>= 1.1.0), BBmisc (>= 1.11), checkmate (>= 1.8.2),
data.table, lhs, parallelMap (>= 1.3)