v0.3.1

Added

• Every fit warns when the model performs worse than a trivial
  baseline on the data it was given (negative log-likelihood against
  class frequencies for probabilistic classifiers, accuracy/R^2
  otherwise). This catches preprocessing mismatches such as passing raw
  features to the inner estimator of a Pipeline that was fit on
  transformed features.
• influence(method='auto') warns when an unsupported model type falls
  back to a refit-based method, and notes that model-agnostic
  estimators can disagree substantially at the per-point level on
  non-linear models; compare estimators and run removal_curve before
  acting on individual rankings.
• Functionals and fitted functional attributors are picklable.
• Row-alignment validation on group functionals and auroc, with clear
  error messages for mismatched audit arrays.

Fixed

• scikit-learn's "X does not have valid feature names" warning is
  suppressed for internal predictions on DataFrame-fitted models.
• stability_replicates deduplicates repeated per-replicate warnings
  and documents its cost model.
• viz.report raises a clear error for functional attributors;
  plot_top_influencers uses a neutral default title for 1-D scores.
• influence_by_group rejects composite (tuple) keys with an
  actionable message.

Documentation

• Every README code block runs as written, top to bottom.
• New worked example: explaining a single prediction.
• Documented pandas conventions (positional indices; the index is not
  used for alignment), the AUROC per-point fidelity considerations, the
  distinction between scores_std_ and stability_replicates, the
  leave-one-out regularization-scaling protocol, and a known issue with
  numpy 2.0.x on macOS (spurious matmul RuntimeWarnings from the
  Accelerate backend).