Package: fairml
Type: Package
Title: Fair Models in Machine Learning
Version: 0.9.1
Date: 2026-03-07
Depends: R (>= 3.5.0)
Imports: methods, glmnet
Suggests: lattice, gridExtra, parallel, cccp, CVXR, survival
Authors@R: person(given = "Marco", family = "Scutari", role = c("aut", "cre"),
                  email = "scutari@bnlearn.com")
Maintainer: Marco Scutari <scutari@bnlearn.com>
Description: 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.
License: MIT + file LICENSE
LazyData: yes
NeedsCompilation: no
Packaged: 2026-07-08 05:41:05 UTC; root
Author: Marco Scutari [aut, cre]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-03-09 08:50:02 UTC
RemoteUrl: https://github.com/cran/fairml
RemoteRef: HEAD
RemoteSha: 9f4156f9208a4df1878da550dfd79f275069f59a