to guide the search process. Additional features include multi-point batch

Title: Bayesian Optimization and Model-Based Optimization of Expensive
('MBO'), also known as Bayesian optimization. It implements the Efficient
Global Optimization Algorithm and 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
person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role = c("aut"), comment = c(ORCID = "0000-0001-6002-6980")),
person("Jakob", "Richter", email = "code@jakob-r.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4481-5554")),
person("Jakob", "Bossek", email = "j.bossek@gmail.com", role = "aut", comment = c(ORCID = "0000-0002-4121-4668")),
person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "aut", comment = c(ORCID = "0000-0003-4511-6245")))
knitr, mco, nnet, party, randomForest, rmarkdown, rgenoud,

Suggests: akima, cmaesr (>= 1.0.3), ggplot2, DiceKriging, DiceOptim,
earth, emoa, GGally, gridExtra, kernlab, kknn, knitr, mco,
nnet, party, randomForest, rmarkdown, rgenoud, rpart, testthat,

Suggests: cmaesr (>= 1.0.3), ggplot2, DiceKriging, earth, emoa, GGally,
gridExtra, kernlab, kknn, knitr, mco, nnet, party,
randomForest, reshape2, rmarkdown, rgenoud, rpart, testthat,

Suggests: akima, cmaesr (>= 1.0.3), ggplot2, DiceKriging, earth, emoa,
GGally, gridExtra, kernlab, kknn, knitr, mco, nnet, party,

Title: Model-Based Optimization for 'mlr3' Through 'mlrMBO'
Description: The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
of general objective functions using the 'bbotk' black box optimization framework. The
control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.
person("Martin", "Binder", email = "developer.mb706@doublecaret.com", role = c("aut", "cre")))
Imports: backports, checkmate, data.table, mlr3misc (>= 0.1.4),
paradox, R6, lhs, callr, bbotk, mlr3tuning
Suggests: mlr, ParamHelpers, testthat, rgenoud, DiceKriging, emoa,
cmaesr, randomForest, smoof, lgr, mlr3, mlr3learners,