Package: clinicalfair
Title: Algorithmic Fairness Assessment for Clinical Prediction Models
Version: 0.1.1
Authors@R: 
    person("Cuiwei", "Gao", , "48gaocuiwei@gmail.com",
    role = c("aut", "cre", "cph"))
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>.
License: MIT + file LICENSE
URL: https://github.com/CuiweiG/clinicalfair,
        https://cuiweig.github.io/clinicalfair
BugReports: https://github.com/CuiweiG/clinicalfair/issues
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
VignetteBuilder: knitr
Config/testthat/edition: 3
Language: en-US
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Repository: https://cuiweig.r-universe.dev
Date/Publication: 2026-04-19 15:47:07 UTC
RemoteUrl: https://github.com/cuiweig/clinicalfair
RemoteRef: HEAD
RemoteSha: 58d718489711f8214d32041025721cd1ae593f62
NeedsCompilation: no
Packaged: 2026-06-18 07:23:54 UTC; root
Author: Cuiwei Gao [aut, cre, cph]
Maintainer: Cuiwei Gao <48gaocuiwei@gmail.com>