clinicalfair: Algorithmic Fairness Assessment for Clinical Prediction Models
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>.
| Version: |
0.1.0 |
| 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 |
| Published: |
2026-04-02 |
| DOI: |
10.32614/CRAN.package.clinicalfair (may not be active yet) |
| Author: |
Cuiwei Gao [aut, cre, cph] |
| Maintainer: |
Cuiwei Gao <48gaocuiwei at gmail.com> |
| BugReports: |
https://github.com/CuiweiG/clinicalfair/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/CuiweiG/clinicalfair |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
clinicalfair citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
clinicalfair results |
Documentation:
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