Package: fairml
Type: Package
Title: Fair Models in Machine Learning
Version: 0.9
Date: 2025-04-29
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: 2025-04-29 23:00:11 UTC; fizban
Author: Marco Scutari [aut, cre]
Repository: CRAN
Date/Publication: 2025-04-29 23:30:12 UTC
Built: R 4.4.3; ; 2025-10-13 10:34:50 UTC; windows
