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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "clinicalfair" in publications use:'
type: software
license: MIT
title: 'clinicalfair: Algorithmic Fairness Assessment for Clinical Prediction Models'
version: 0.1.1
identifiers:
- type: doi
  value: 10.32614/CRAN.package.clinicalfair
abstract: 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) <https://doi.org/10.1126/science.aax2342>
  and Hardt, Price, and Srebro (2016) <https://doi.org/10.48550/arXiv.1610.02413>.
authors:
- family-names: Gao
  given-names: Cuiwei
  email: 48gaocuiwei@gmail.com
preferred-citation:
  type: manual
  title: 'clinicalfair: Algorithmic Fairness Assessment for Clinical Prediction Models'
  authors:
  - family-names: Gao
    given-names: Cuiwei
    email: 48gaocuiwei@gmail.com
  year: '2026'
  notes: R package version 0.1.1
  url: https://github.com/CuiweiG/clinicalfair
repository: https://cuiweig.r-universe.dev
repository-code: https://github.com/CuiweiG/clinicalfair
commit: 58d718489711f8214d32041025721cd1ae593f62
url: https://cuiweig.github.io/clinicalfair
date-released: '2026-04-19'
contact:
- family-names: Gao
  given-names: Cuiwei
  email: 48gaocuiwei@gmail.com