v0.3.0

Scalar-property attribution is now a first-class, domain-neutral
engine; the fairness module is a thin vocabulary layer over it. No
compatibility shims.

Added

• Engine (top level): Functional (a scalar function of per-sample
  model scores or losses on a reference set, with optional analytic
  gradient), attributed by FunctionalInfluence (closed form),
  RefitFunctionalInfluence (exact leave-one-out), and
  SubsampledFunctionalInfluence (Monte-Carlo subsets); evaluated by
  functional_value(model, X, F, y). fit is functional-independent:
  one fitted attributor can explain any number of functionals via
  explain(..., functional=...).
• pyinfluence.functionals: builders with analytic gradients —
  mean(of), group_gap(groups, keep=None, of), cohens_d(groups),
  worst_group_mean(groups, of), and auroc(pos_label). Each is
  validated against exact refitting in the test suite.

Changed (breaking)

• pyinfluence.fairness now provides vocabulary and workflow only:
  disparity(metric, sensitive, *, target_of=... | pos_label=...) maps
  'dp'/'eopp'/'fpr'/'worst_group_loss' onto the builders;
  disparity_value(_hard), group_removal_effect, and
  disparity_removal_curve keep their signatures (metric accepts a
  name or any Functional).
• Removed FairnessInfluenceFunctions, RefitFairnessInfluence, and
  SubsampledFairnessInfluence (replaced by the engine classes plus
  disparity), the raw-callable metric form, and the top-level
  re-exports of fairness utilities.
• cohens_d moved to pyinfluence.functionals as a builder returning
  a Functional with an analytic gradient.