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: | 0.7 |
| Depends: | R (≥ 3.5.0) |
| Imports: | methods, optiSolve, CVXR, glmnet |
| Suggests: | lattice, parallel |
| Published: | 2022-09-10 |
| Author: | Marco Scutari [aut, cre] |
| Maintainer: | Marco Scutari <scutari at bnlearn.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | fairml results |
| Reference manual: | fairml.pdf |
| Package source: | fairml_0.7.tar.gz |
| Windows binaries: | r-devel: fairml_0.6.3.zip, r-release: fairml_0.7.zip, r-oldrel: fairml_0.6.3.zip |
| macOS binaries: | r-release (arm64): fairml_0.6.3.tgz, r-oldrel (arm64): fairml_0.6.3.tgz, r-release (x86_64): fairml_0.6.3.tgz, r-oldrel (x86_64): fairml_0.6.3.tgz |
| Old sources: | fairml archive |
| Reverse suggests: | mlr3fairness |
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