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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 "civic.icarm" in publications use:'
type: software
license: MIT
title: 'civic.icarm: Interpretable Civic-Accountable and Responsible Machine Learning'
version: 0.2.0
doi: 10.32614/CRAN.package.civic.icarm
abstract: 'A general-purpose framework for Interpretable Civic-Accountable and Responsible
  Machine Learning (ICARM). Works with any clean tabular data and automatically detects
  whether a task is binary classification, multi-class classification, or regression
  from the target variable type. Provides a single unified entry point civic_fit()
  alongside tidy interfaces for global and local model explanations, group-level fairness
  auditing, probability calibration, multi-model comparison, threshold analysis, and
  reproducible audit trails. Designed to support the DataCitizen-Pro research agenda
  at Ludwigsburg University of Education: developing data literacy, statistical reasoning,
  and democratic judgment formation in civic and political teacher education. References:
  Biecek (2018) <https://doi.org/10.18637/jss.v085.i04>, Kuhn (2008) <https://doi.org/10.18637/jss.v028.i05>,
  Awe (2025) <https://github.com/Olawaleawe/civic.icarm>.'
authors:
- family-names: Awe
  given-names: Olushina Olawale
  email: olawaleawe@gmail.com
repository: https://olawaleawe.r-universe.dev
commit: 7a5bbe4ae127dc282cc11e760098253e319d95c1
date-released: '2026-06-18'
contact:
- family-names: Awe
  given-names: Olushina Olawale
  email: olawaleawe@gmail.com