Package: fairmetrics Title: Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes Version: 1.0.8 Authors@R: c( person("Jianhui", "Gao", email = "jianhui.gao@mail.utoronto.ca", role = c("aut"),comment = c(ORCID = "0000-0003-0915-1473")), person("Benjamin", "Smith", email = "benyamin.smith@mail.utoronto.ca", role = c("aut","cre"), comment = c(ORCID = "0009-0007-2206-0177")), person("Benson", "Chou", email = "benson.chou@mail.utoronto.ca", role = c("aut"), comment = c(ORCID = "0009-0007-0265-033X")), person("Jessica", "Gronsbell", email = "j.gronsbell@utoronto.ca", role = c("aut"), comment = c(ORCID = "0000-0002-5360-5869")) ) Description: 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 (2025) . Imports: stats License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 Suggests: dplyr, magrittr, corrplot, randomForest, pROC, SpecsVerification, knitr, rmarkdown, testthat, kableExtra, naniar Config/testthat/edition: 3 Depends: R (>= 3.5.0) LazyData: true URL: https://jianhuig.github.io/fairmetrics/ VignetteBuilder: knitr Repository: https://jianhuig.r-universe.dev Date/Publication: 2026-04-17 20:59:59 UTC RemoteUrl: https://github.com/jianhuig/fairmetrics RemoteRef: HEAD RemoteSha: e32f070bd668acef95c98c193c43ece3873e4105 NeedsCompilation: no Packaged: 2026-06-17 07:45:24 UTC; root Author: Jianhui Gao [aut] (ORCID: ), Benjamin Smith [aut, cre] (ORCID: ), Benson Chou [aut] (ORCID: ), Jessica Gronsbell [aut] (ORCID: ) Maintainer: Benjamin Smith