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:	1.0.0
Depends:	R (≥ 3.5.0)
Imports:	stats
Suggests:	dplyr, magrittr, corrplot, randomForest, pROC, SpecsVerification, knitr, rmarkdown, testthat, kableExtra, naniar
Published:	2025-05-19
DOI:	10.32614/CRAN.package.fairmetrics
Author:	Jianhui Gao [aut], Benjamin Smith [aut, cre], Benson Chou [aut], Jessica Gronsbell [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_1.0.0.tar.gz
Windows binaries:	r-devel: fairmetrics_1.0.0.zip, r-release: fairmetrics_1.0.0.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): fairmetrics_1.0.0.tgz, r-oldrel (arm64): fairmetrics_1.0.0.tgz, r-release (x86_64): fairmetrics_1.0.0.tgz, r-oldrel (x86_64): fairmetrics_1.0.0.tgz

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