mcboost: Multi-Calibration Boosting

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.

Version:	0.4.2
Depends:	R (≥ 3.1.0)
Imports:	backports, checkmate (≥ 2.0.0), data.table (≥ 1.13.6), mlr3 (≥ 0.10), mlr3misc (≥ 0.8.0), mlr3pipelines (≥ 0.3.0), R6 (≥ 2.4.1), rmarkdown, rpart, glmnet
Suggests:	curl, lgr, formattable, tidyverse, PracTools, mlr3learners, mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr, testthat (≥ 3.1.0)
Published:	2022-08-18
Author:	Florian Pfisterer [cre, aut], Susanne Dandl [ctb], Christoph Kern [ctb], Carolin Becker [ctb], Bernd Bischl [ctb]
Maintainer:	Florian Pfisterer <pfistererf at googlemail.com>
BugReports:	https://github.com/mlr-org/mcboost/issues
License:	LGPL (≥ 3)
URL:	https://github.com/mlr-org/mcboost
NeedsCompilation:	no
Citation:	mcboost citation info
Materials:	README NEWS
CRAN checks:	mcboost results

Documentation:

Reference manual:	mcboost.pdf
Vignettes:	MCBoost - Basics and Extensions MCBoost - Health Survey Example

Downloads:

Package source:	mcboost_0.4.2.tar.gz
Windows binaries:	r-devel: mcboost_0.4.2.zip, r-release: mcboost_0.4.2.zip, r-oldrel: mcboost_0.4.2.zip
macOS binaries:	r-release (arm64): mcboost_0.4.2.tgz, r-oldrel (arm64): mcboost_0.4.2.tgz, r-release (x86_64): mcboost_0.4.2.tgz, r-oldrel (x86_64): mcboost_0.4.2.tgz
Old sources:	mcboost archive

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