fairmetrics: Fairness Evaluation Metrics with Confidence Intervals

A collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.

Version: 1.0.3
Depends: R (≥ 3.5.0)
Imports: stats
Suggests: dplyr, magrittr, corrplot, randomForest, pROC, SpecsVerification, knitr, rmarkdown, testthat, kableExtra, naniar
Published: 2025-06-12
DOI: 10.32614/CRAN.package.fairmetrics
Author: Jianhui Gao ORCID iD [aut], Benjamin Smith ORCID iD [aut, cre], Benson Chou ORCID iD [aut], Jessica Gronsbell ORCID iD [aut]
Maintainer: Benjamin Smith <benyamin.smith at mail.utoronto.ca>
License: MIT + file LICENSE
URL: https://jianhuig.github.io/fairmetrics/
NeedsCompilation: no
CRAN checks: fairmetrics results

Documentation:

Reference manual: fairmetrics.pdf
Vignettes: Binary Protected Attributes (source, R code)

Downloads:

Package source: fairmetrics_1.0.3.tar.gz
Windows binaries: r-devel: fairmetrics_1.0.3.zip, r-release: fairmetrics_1.0.3.zip, r-oldrel: fairmetrics_1.0.3.zip
macOS binaries: r-release (arm64): fairmetrics_1.0.3.tgz, r-oldrel (arm64): fairmetrics_1.0.3.tgz, r-release (x86_64): fairmetrics_1.0.3.tgz, r-oldrel (x86_64): fairmetrics_1.0.3.tgz
Old sources: fairmetrics archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=fairmetrics to link to this page.