Package: civic.icarm
Title: Interpretable Civic-Accountable and Responsible Machine Learning
Version: 0.2.0
Authors@R: c(person("Olushina Olawale", "Awe", email = "olawaleawe@gmail.com", role = c("aut", "cre")), person("Ludwigsburg University of Education", role = "fnd"))
Description: 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) <doi:10.18637/jss.v085.i04>,
    Kuhn (2008) <doi:10.18637/jss.v028.i05>,
    Awe (2025) <https://github.com/Olawaleawe/civic.icarm>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
Imports: stats, utils, rpart, ggplot2, dplyr, tidyr, tibble, purrr,
        rlang, jsonlite, digest
Suggests: DALEX, glmnet, mgcv, pROC, nnet, testthat, covr
Config/testthat/edition: 3
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-06-10 09:03:39 UTC; olawa
Author: Olushina Olawale Awe [aut, cre],
  Ludwigsburg University of Education [fnd]
Maintainer: Olushina Olawale Awe <olawaleawe@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-17 17:50:06 UTC
