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clinicalfair  

Algorithmic Fairness Assessment for Clinical Prediction Models
View on CRAN: Click here


Download and install clinicalfair package within the R console
Install from CRAN:
install.packages("clinicalfair")

Install from Github:
library("remotes")
install_github("cran/clinicalfair")

Install by package version:
library("remotes")
install_version("clinicalfair", "0.1.0")



Attach the package and use:
library("clinicalfair")
Maintained by
Cuiwei Gao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-02
Latest Update: 2026-04-02
Description:
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>.
How to cite:
Cuiwei Gao (2026). clinicalfair: Algorithmic Fairness Assessment for Clinical Prediction Models. R package version 0.1.0, https://cran.r-project.org/web/packages/clinicalfair. Accessed 16 Jul. 2026.
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