v0.2.0

Correctness release; the numerical claims below are enforced by tests
against exact refitting or brute-force enumeration.

Breaking changes

• Fairness explain audit arguments are keyword-only.
• viz.plot_heatmap no longer accepts cluster=.
• BanzhafInfluence returns NaN (with a warning) instead of 0.0 for
  training points whose subset refits all fail.
• InfluenceFunctions rejects configurations it cannot represent
  (class_weight, l1/elasticnet penalties) instead of returning
  degraded scores; influence(method='auto') falls back with a
  warning.
• removal_curve requires mode='loss'.
• Labels outside model.classes_ raise instead of being mapped to the
  negative class.
• viz requires matplotlib >= 3.9.

Fixed

• Binary label encodings other than {0,1} produced incorrect scores for
  LogisticRegression (train- and test-side NLL gradients used raw
  label values). Scores are now invariant across {0,1}, {1,2}, {-1,+1},
  and string encodings; the same fix applies to the fairness closed
  form.
• Fairness eopp and fpr were swapped for non-{0,1} encodings
  (conditioning on the literal value 1 instead of the model's positive
  class); disparity_value_hard's worst-group error compared decisions
  against raw labels.
• NaN scores corrupted the analysis utilities (top_influential ranked
  NaN as most helpful; a single NaN emptied find_mislabeled and
  poisoned influence_summary/compare_attributors). All utilities
  and plots exclude NaN from rankings and statistics, with a warning.
• Small-subset sampling bias in BanzhafInfluence (subsets of size one
  were excluded from Monte-Carlo sampling).
• disparity_removal_curve reported the wrong base_disparity when
  fractions did not start at 0.
• Clear early errors replace cryptic failures for sparse inputs,
  multi-output targets, unfitted models passed to refit-based
  attributors, multiclass classifiers without predict_proba, and
  wrapped estimators under method='auto'.
• KernelRidge with a callable kernel failed, and kernel_params was
  ignored; the kernel reconstruction now mirrors scikit-learn's.
• liblinear-solver logistic models trigger a warning (the regularized
  intercept violates the Hessian assumption).

Added

• Custom fairness metrics via callables, with analytic or
  finite-difference gradients in the closed form; bundled cohens_d.
• Monte-Carlo standard errors: BanzhafInfluence and
  BootstrapInfluence expose scores_std_;
  viz.plot_top_influencers(..., xerr=...) draws error bars.
• New plots: plot_disparity_curve, plot_detection_curve,
  plot_influence_concentration.
• stability_replicates utility feeding plot_top_k_stability.
• supports(model): capability check for InfluenceFunctions that
  never raises or warns.
• self_influence computes the diagonal directly for
  InfluenceFunctions, LOOInfluence, and BootstrapInfluence (O(n) memory
  instead of the full matrix).
• README figures and an explicit limitations section.