fairml: Fair Models in Machine Learning

Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al. (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own approach that uses ridge regression to enforce fairness.

Version: 0.6
Depends: R (≥ 3.5.0)
Imports: methods, optiSolve, CVXR, glmnet
Suggests: lattice, parallel
Published: 2021-08-09
Author: Marco Scutari [aut, cre]
Maintainer: Marco Scutari <marco.scutari at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: fairml results

Documentation:

Reference manual: fairml.pdf

Downloads:

Package source: fairml_0.6.tar.gz
Windows binaries: r-devel: fairml_0.6.zip, r-release: fairml_0.6.zip, r-oldrel: fairml_0.6.zip
macOS binaries: r-release (arm64): fairml_0.6.tgz, r-release (x86_64): fairml_0.6.tgz, r-oldrel: fairml_0.6.tgz
Old sources: fairml archive

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