First stable release.
- Five-dimension validation: coherence, consistency, convergent validity, adversarial discrimination, stability/sensitivity
- Cohen's kappa and Fleiss' kappa with bootstrap confidence intervals
- Configurable thresholds per dimension with PASS / MARGINAL / FAIL verdicts
- Evaluation runner with foundation + advanced dimension orchestration
- Zero external dependencies (Python standard library only)
- Stratified sampling module for expert review coverage
- Automatic sufficiency checks (uncovered strata, underrepresented categories)
- Sample plan export for review coordination
- Pluggable rule engine with decorator-based registration
- Per-item and cross-item validation rules
- Severity levels (error, warning, info)
- Kappa and Jaccard similarity against external references
- Per-item agreement tracking with disagreement detail
- Minimal pairs testing with expected label assertions
- Ambiguity cases with multiple acceptable answers
- Discrimination rate scoring
- Paraphrase invariance testing
- Perturbation sensitivity with expected direction assertions
- Multi-dimension label comparison (domain, subdomain, function)
- NiceGUI dashboard with dimension radar chart
- Configurable thresholds with live verdict re-rendering
- Per-control issue view aggregated across dimensions
- Adversarial detail panel with minimal pair results
- Expert review coverage panel from sampling module
- AICM-to-FAIR-CAM demonstration: 20 CSA AI Controls Matrix controls mapped to FAIR-CAM functional domains
- Claude LLM classifier example (
examples/llm_classifier.py)