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 from Scutari, Panero and Proissl (2022) <doi:10.1007/s11222-022-10143-w> that uses ridge regression to enforce fairness.
Version: 0.9 Depends: R (≥ 3.5.0) Imports: methods, glmnet Suggests: lattice, gridExtra, parallel, cccp, CVXR, survival Published: 2025-04-29 DOI: 10.32614/CRAN.package.fairml Author: Marco Scutari [aut, cre] Maintainer: Marco Scutari <scutari at bnlearn.com> License: MIT + file LICENSE NeedsCompilation: no Materials: ChangeLog CRAN checks: fairml results Documentation: Reference manual: fairml.pdf Downloads: Package source: fairml_0.9.tar.gz Windows binaries: r-devel: fairml_0.9.zip, r-release: fairml_0.9.zip, r-oldrel: fairml_0.9.zip macOS binaries: r-release (arm64): fairml_0.9.tgz, r-oldrel (arm64): fairml_0.9.tgz, r-release (x86_64): fairml_0.9.tgz, r-oldrel (x86_64): fairml_0.9.tgz Old sources: fairml archive Reverse dependencies: Reverse depends: dsld Reverse suggests: mlr3fairness Linking:Please use the canonical form https://CRAN.R-project.org/package=fairml to link to this page.
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