person("Daniel", "Furtado", email = "danielff@dex.ufla.br", role = "aut",
on studentized 'midrange' and 'range' distributions.
The tests are: Tukey Midrange ('TM' test),
Means Grouping Midrange ('MGM' test) and
Means Grouping Range ('MGR' test). The first two tests were published by
Imports: stats, utils, graphics, grDevices, SMR, writexl, xtable, tcltk
Suggests: tkrplot, testthat, knitr, rmarkdown, covr

Means Grouping Range ('MGR' test). The articles of these

Title: Mixed Integer Evolution Strategies
Description: Evolutionary black box optimization algorithms building on the
'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
algorithms, as well as their fundamental building blocks that can be used to
manually construct specialized optimization loops. The Mixed Integer Evolution
Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
implemented, as well as the multi-objective optimization algorithms NSGA-II
by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.
person("Martin", "Binder", email = "mlr.developer@mb706.com", role = c("aut", "cre")),
person(given = "Lennart", family = "Schneider", role = c("ctb"), email = "lennart.sch@web.de", comment = c(ORCID = "0000-0003-4152-5308")),
person(given = "Susanne", family = "Dandl", role = c("ctb"), email = "dandl.susanne@googlemail.com", comment = c(ORCID = "0000-0003-4324-4163")),
person(given = "Andreas", family = "Hofheinz", role = c("ctb"), email = "andreas.hofheinz@outlook.com")
Imports: mlr3misc (>= 0.5.0), checkmate (>= 1.9.0), R6, bbotk (>=
Suggests: tinytest, mlr3tuning, mlr3, mlr3learners, ranger, xgboost,
Collate: 'dictionaries.R' 'MiesOperator.R' 'Filtor.R' 'FiltorMaybe.R'
'bibentries.R' 'repr.R' 'utils.R' 'utils_mo.R' 'zzz.R'

Title: Multiple Imputation for Exploratory Factor Analysis
person("Vahid", "Nassiri", role = "aut"),
person("Geert ", "Molenberghs", role = "aut"),
person("Geert ", "Verbeke", role = "aut"),
person("Tobias", "Busch", email = "teebusch@gmail.com",
Suggests: psych, testthat, knitr, rmarkdown, ggplot2, tidyr, covr
Description: Impute the covariance matrix of incomplete data so that factor
analysis can be performed. Imputations are made using multiple imputation
by Multivariate Imputation with Chained Equations (MICE) and combined with
Rubin's rules. Parametric Fieller confidence intervals and nonparametric
bootstrap confidence intervals can be obtained for the variance explained by
different numbers of principal components. The method is described in