fairmetrics: Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes

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 for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.

Version: 1.0.6
Depends: R (≥ 3.5.0)
Imports: stats
Suggests: dplyr, magrittr, corrplot, randomForest, pROC, SpecsVerification, knitr, rmarkdown, testthat, kableExtra, naniar
Published: 2025-09-11
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
Citation: fairmetrics citation info
CRAN checks: fairmetrics results

Documentation:

Reference manual: fairmetrics.html , fairmetrics.pdf
Vignettes: Assessing Model Fairness Across Binary Protected Attributes (source, R code)

Downloads:

Package source: fairmetrics_1.0.6.tar.gz
Windows binaries: r-devel: fairmetrics_1.0.6.zip, r-release: fairmetrics_1.0.6.zip, r-oldrel: fairmetrics_1.0.6.zip
macOS binaries: r-release (arm64): fairmetrics_1.0.6.tgz, r-oldrel (arm64): fairmetrics_1.0.6.tgz, r-release (x86_64): fairmetrics_1.0.6.tgz, r-oldrel (x86_64): fairmetrics_1.0.6.tgz
Old sources: fairmetrics archive

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