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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | civic.icarm_0.2.0.tar.gz |
148.9 KiB |
0.2.0 |
rolling linux/noble R-4.5 | civic.icarm_0.2.0.tar.gz |
149.0 KiB |
0.2.0 |
rolling source/ R- | civic.icarm_0.2.0.tar.gz |
49.4 KiB |
0.2.0 |
latest linux/jammy R-4.5 | civic.icarm_0.2.0.tar.gz |
148.9 KiB |
0.2.0 |
latest linux/noble R-4.5 | civic.icarm_0.2.0.tar.gz |
149.0 KiB |
0.2.0 |
latest source/ R- | civic.icarm_0.2.0.tar.gz |
49.4 KiB |
0.2.0 |
2026-04-23 source/ R- | civic.icarm_0.2.0.tar.gz |
0 B |