"Joëlle", "Barido-Sottani", role = "rev",
comment = c("Joëlle reviewed the package for rOpenSci, see https://github.com/ropensci/software-review/issues/360")
"Vikram reviewed the package for rOpenSci, see https://github.com/ropensci/software-review/issues/360",
'mcbette' allows to do a Bayesian model comparison over some
Imports: babette (>= 2.1.5), beautier (>= 2.4), beastier (>= 2.2.1),
curl, devtools, mauricer (>= 2.3), Rmpfr, testit, txtplot
Suggests: ape, ggplot2, hunspell, knitr, lintr, nLTT, phangorn,
rappdirs, rmarkdown, spelling, stringr, testthat (>= 2.1.0),
Vikram Baliga [rev] (Vikram reviewed the package for rOpenSci, see

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

Title: Matrix Computation Based Identification Of Prime Implicants
Description: 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.
Packaged: 2011-12-29 01:52:01 UTC; schwender

Packaged: 2012-01-02 17:56:47 UTC; schwender

Packaged: 2012-01-03 20:41:29 UTC; schwender

Title: Matrix Computation Based Identification of Prime Implicants
Packaged: 2018-09-14 23:15:47 UTC; schwender

Packaged: 2018-10-06 19:21:54 UTC; schwender

Description: 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.
Imports: backports, checkmate (>= 2.0.0), lifecycle, data.table (>=
Suggests: curl, formattable, tidyverse, PracTools, mlr3learners,
mlr3oml, neuralnet, paradox, testthat, knitr, ranger,
Author: Florian Pfisterer [cre, aut] (<https://orcid.org/0000-0001-8867-762X>),