modelcardr: Machine Learning Prediction Auditing and Model Card Reporting

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" <doi:10.1145/3287560.3287596>.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	graphics, stats, tools, utils, withr
Suggests:	testthat (≥ 3.0.0), jsonlite
Published:	2026-07-30
DOI:	10.32614/CRAN.package.modelcardr
Author:	Georgios P. Georgiou [aut, cre]
Maintainer:	Georgios P. Georgiou <georgiou.georg at unic.ac.cy>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	modelcardr results

Documentation:

Reference manual:

modelcardr.html , modelcardr.pdf

Downloads:

Package source:	modelcardr_0.1.0.tar.gz
Windows binaries:	r-devel: modelcardr_0.1.0.zip, r-release: modelcardr_0.1.0.zip, r-oldrel: modelcardr_0.1.0.zip
macOS binaries:	r-release (arm64): modelcardr_0.1.0.tgz, r-oldrel (arm64): modelcardr_0.1.0.tgz, r-release (x86_64): modelcardr_0.1.0.tgz, r-oldrel (x86_64): modelcardr_0.1.0.tgz

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