fairscope v0.1.0

First release of fairscope — subgroup-stratified, calibration-aware fairness auditing for ML models, grounded in peer-reviewed methods.

core/

DeLong AUC confidence intervals + paired/unpaired tests, a stratified bootstrap AUC test, Expected/Maximum Calibration Error with reliability diagrams, temperature-scaling and isotonic recalibration, Bonferroni and Benjamini–Hochberg corrections, and subgroup metrics (symmetric disparate impact, equalized odds difference). 100% test coverage.

healthcare/

HealthcareFairnessAudit + HealthcareReport — a one-call clinical fairness audit composing core/ (per-subgroup DeLong CIs, ECE, Bonferroni-corrected pairwise tests, Brier/F1), with report tables, an AUC forest plot, reliability-curve plots, multi-page PDF export, and an optional SHAP summary (fairscope[shap]).

Top-level FairnessAudit(model, domain="healthcare", ...) dispatcher.

Methods are ported from published work and cited in docstrings; only the forthcoming five-axis CPFE protocol is presented as novel. Wheel and source distribution attached.