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 + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | fairml results |
| Reference manual: | fairml.html ,fairml.pdf |
| 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 suggests: | classmap,mlr3fairness |
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