babette (>= 2.3), beautier (>= 2.6.2), beastier (>= 2.4.6),
curl, devtools, mauricer (>= 2.5), Rmpfr, testit, txtplot
ape, ggplot2, hunspell, knitr, lintr, markdown, nLTT,
phangorn, rappdirs, rmarkdown, spelling, stringr, testthat (>=
2.1.0), tracerer

Matrix Computation Based Identification Of Prime Implicants
Computes the prime implicants or a minimal disjunctive normal form for a
logic expression presented by a truth table or a logic tree. Has been particularly
developed for logic expressions resulting from a logic regression analysis, i.e.
logic expressions typically consisting of up to 16 literals, where the prime implicants
are typically composed of a maximum of 4 or 5 literals.

Matrix Computation Based Identification of Prime Implicants

c(person(given = "Florian",
family = "Pfisterer",
role = c("cre", "aut"),
email = "pfistererf@googlemail.com",
comment = c(ORCID = "0000-0001-8867-762X")),
person(given = "Susanne",
family = "Dandl",
role = "ctb",
email = "susanne.dandl@stat.uni-muenchen.de",
comment = c(ORCID = "0000-0003-4324-4163")),
person(given = "Christoph",
family = "Kern",
role = "ctb",
email = "c.kern@uni-mannheim.de",
comment = c(ORCID = "0000-0001-7363-4299")),
person(given = "Bernd",
family = "Bischl",
role = "ctb",
email = "bernd_bischl@gmx.net",
comment = c(ORCID = "0000-0001-6002-6980"))
)
Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
'Multi-Accuracy Boosting' (2019) <arXiv:1805.12317> for the multi-calibration of a machine learning model's prediction.
'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
backports, checkmate (>= 2.0.0), lifecycle, data.table (>=
1.13.6), mlr3 (>= 0.10), mlr3misc (>= 0.8.0), mlr3pipelines (>=
0.3.0), R6 (>= 2.4.1), rpart, glmnet
curl, formattable, tidyverse, PracTools, mlr3learners,
mlr3oml, neuralnet, paradox, testthat, knitr, ranger,
rmarkdown, covr