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

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

Model-Based Optimization for 'mlr3' Through 'mlrMBO'
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'.
c(
person("Martin", "Binder", email = "developer.mb706@doublecaret.com", role = c("aut", "cre")))
backports, checkmate, data.table, mlr3misc (>= 0.1.4),
paradox, R6, lhs, callr, bbotk, mlr3tuning
mlr, ParamHelpers, testthat, rgenoud, DiceKriging, emoa,
cmaesr, randomForest, smoof, lgr, mlr3, mlr3learners,
mlr3pipelines, mlrMBO, ranger, rpart
'utils.R' 'CapsuledMlr3Learner.R' 'ParamHelpersParamSet.R'
'optimize.R' 'paramset.R' 'TunerInterMBO.R' 'surrogates.R'
'zzz.R'

Stepwise Regression with Assumptions Checking
c(person("Thidarat","Thongsri",role = c("aut", "cre"),
email = "k.th.thidarat@gmail.com"),
person("Klairung", "Samart", role = c("aut")))
The stepwise regression with assumptions checking and the possible Box-Cox transformation.
rmarkdown, knitr, testthat (>= 3.0.0), spelling
Thidarat Thongsri [aut, cre],
Klairung Samart [aut]

Efficient Estimation of Bayesian SBMs & MLSBMs
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).