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# CITATION.cff 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 "modelcardr" in publications use:'
type: software
license: MIT
title: 'modelcardr: Machine Learning Prediction Auditing and Model Card Reporting'
version: 0.1.0
doi: 10.32614/CRAN.package.modelcardr
abstract: Audits predictions from fitted machine learning models without requiring
  retraining or access to the fitted model object. It supports binary classification,
  multiclass classification, and regression, providing performance metrics with bootstrap
  confidence intervals, calibration and threshold analyses, residual diagnostics,
  subgroup comparisons, user-defined acceptance criteria, risk warnings, and self-contained
  reports. For binary-classification and regression models, the package can also assemble
  and render structured model cards documenting intended use, evaluation data, performance,
  subgroup results, assumptions, and limitations. The model-card reporting approach
  is described by Mitchell et al. (2019) "Model Cards for Model Reporting" <https://doi.org/10.1145/3287560.3287596>.
authors:
- family-names: Georgiou
  given-names: Georgios P.
  email: georgiou.georg@unic.ac.cy
repository: https://cran.r-universe.dev
commit: edae76d9c5893ceb51f6f9c4a2c157375f037b46
date-released: '2026-07-30'
contact:
- family-names: Georgiou
  given-names: Georgios P.
  email: georgiou.georg@unic.ac.cy