Summarize ranking stability across explicit preference sweeps - #163
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preference_sweepassesses existing designs across explicit preference vectors and normalization choices, reporting ranks, shared selection credit for ties, regret, feasible Pareto alternatives, and pairwise practical-equivalence thresholds in raw units. Output retains actual normalization anchors, effective weights, copied assumptions, and per-design metadata exportable through the existing DataFrame API. Session raw means override weighted columns.Optional paired uncertainty uses existing comparisons over eligible raw replicates, with nominal confidence adjustment across observables and alternatives. Point-estimate equivalence, preference frequencies, bootstrap/t assumptions, optional stopping and selected-reference limits are documented explicitly. No simulator or optimizer is added.
Closes #155.
Validation:
just ci(579 tests, 98.86% coverage) and strict docs build. Tests cover mixed directions/units, normalization-dependent choices, reference extrapolation, weight perturbations, ties/zero-range objectives, infeasibility/non-finite scores, raw adaptive values, replicated means, paired uncertainty, malformed inputs and explicit inference assumptions.