Package: clinicalfair
Title: Algorithmic Fairness Assessment for Clinical Prediction Models
Version: 0.1.0
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
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
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-03-30 12:34:39 UTC; openclaw
Author: Cuiwei Gao [aut, cre, cph]
Maintainer: Cuiwei Gao <48gaocuiwei@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-02 20:10:09 UTC
Built: R 4.6.0; ; 2026-04-29 00:43:37 UTC; unix
