NEWS

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modelcardr 0.4.0

• Added unified auditing for binary classification, multiclass classification, and regression.
• Added automatic task detection with explicit overrides for numeric class codes.
• Added multiclass confusion matrices, class-level precision/recall/F1, macro and weighted metrics, one-vs-rest AUC, multiclass log loss and Brier score, and top-label calibration.
• Added regression MAE, RMSE, MSE, R-squared, bias, MAPE, SMAPE, normalized errors, residual diagnostics, and subgroup error analysis.
• Extended pass/fail requirements and plain-language risk warnings to all supported tasks.
• Updated HTML, Markdown, JSON, and plotting behavior to be task-specific.