Characterize static evaluation semantics

[uriahf]

Goal

Implement Stage 1A of the cross-language semantic audit as characterization tests only.

Coverage

Adds focused tests for:

• one model × one population
• multiple models × the same population
• the same model/prediction rule across populations with different prevalence
• distinct populations with equal prevalence
• explicitly paired model-population inputs
• calibration grouping and the global identity line
• performance-table input semantics
• ROC, precision-recall, gains, lift, decision, and interventions-avoided grouping/reference ownership
• current model trace labels and color behavior

The tests deliberately preserve Python's current generic reference_group representation while distinguishing shared-population model cases from multi-population reference ownership.

Scope

Test-only. No statistical calculations, public APIs, production behavior, plotting code, package metadata, generated documentation, or rtichoke_viz are changed.

Any surprising current behavior is characterized rather than fixed in this PR.

Paired PR

R: uriahf/rtichoke#163

[github-actions]

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2026-08-21 10:52 UTC