civic.icarm: Interpretable Civic-Accountable and Responsible Machine Learning
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>.
| Version: |
0.2.0 |
| 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 |
| Published: |
2026-06-17 |
| DOI: |
10.32614/CRAN.package.civic.icarm (may not be active yet) |
| Author: |
Olushina Olawale Awe [aut, cre],
Ludwigsburg University of Education [fnd] |
| Maintainer: |
Olushina Olawale Awe <olawaleawe at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README |
| CRAN checks: |
civic.icarm results |
Documentation:
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