Man pages for civic.icarm
Interpretable Civic-Accountable and Responsible Machine Learning
| civic_audit | Generate a reproducible audit trail |
| civic_calibrate | Assess probability calibration (binary classification) |
| civic_compare | Compare multiple civic_models on a shared test set |
| civic_education | civic_education dataset |
| civic_equalized_odds_curve | Compute equalized odds curves across thresholds (binary only) |
| civic_equity_summary | Summarise fairness into scalar equity indicators |
| civic_explain | Generate global model explanations |
| civic_explain_local | Generate local instance-level explanations |
| civic_fairness | Compute group-level fairness metrics |
| civic_fit | Fit an interpretable ICARM model — works with any tabular... |
| civic_german_credit | civic_german_credit dataset |
| civic.icarm-package | civic.icarm: Interpretable Civic-Accountable and Responsible... |
| civic_metrics | Compute performance metrics for any task type |
| civic_plots | Visualisation functions for civic.icarm |
| civic_scorecard | Generate a full civic accountability scorecard |
| civic_split | Reproducible train/test split |
| civic_thresholds | Threshold sweep for binary classification |
| civic_voting | civic_voting dataset |
| predict.civic_model | Predict from a civic_model |
| print.civic_model | Print a civic_model |
| summary.civic_model | Summary of a civic_model |
