Algorithmic Fairness Assessment for Clinical Prediction Models R package clinicalfair version 0.1.0 Post-hoc fairness auditing toolkit for clinical prediction models. Unlike in-processing approaches that modify model training, this package evaluates existing models by computing group-wise fairness metrics (demographic parity, equalized odds, predictive parity, calibration disparity), visualizing disparities across protected attributes, and performing threshold-based mitigation. Supports intersectional analysis across multiple attributes and generates audit reports useful for fairness-oriented auditing in clinical AI settings. Methods described in Obermeyer et al. (2019) <doi:10.1126/science.aax2342> and Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>. Software Depends: R (>= 4.1.0) Imports: cli (>= 3.4.0), dplyr (>= 1.1.0), ggplot2 (>= 3.4.0), rlang (>= 1.1.0), stats, tibble (>= 3.1.0) Suggests: knitr, rmarkdown, testthat (>= 3.0.0), withr Cuiwei Gao <48gaocuiwei@gmail.com> Comprehensive R Archive Network (CRAN) Cuiwei Gao [aut, cre, cph] MIT + file LICENSE (https://CRAN.R-project.org/package=clinicalfair/LICENSE) 2026-04-02 application/tgz https://CRAN.R-project.org/package=clinicalfair doi:10.32614/CRAN.package.clinicalfair en-US