Changes in version 0.7.2                        

  - Added mlp as an internal torch-backed learner for regression, binary
    classification, and multiclass classification.
  - Added CRAN installation instructions to the README and kept the
    GitHub installation path for development snapshots.
  - Added a README note that the funcml companion paper is submitted to
    JMLR.

                 Changes in version 0.7.1 (2026-04-21)                  

  - Refined the README into a more detailed progressive API walkthrough
    with additional tables, figures, and staged examples covering the
    full package surface.
  - Hardened interpretability runtime paths by forcing vip to use
    permutation importance consistently while retaining shapviz-enhanced
    SHAP plotting when the optional plotting packages are installed.

                        Changes in version 0.7.0                        

  - Consolidated funcml as a machine learning framework for R with
    stable S3 interfaces for fitting, prediction, evaluation, tuning,
    learner comparison, interpretation, and plug-in g-computation.
  - Added richer resampling support through plain holdout, grouped
    cross-validation, and time-aware rolling splits.
  - Added uncertainty summaries to evaluate() and compare_learners(),
    including fold-level standard errors and confidence intervals in
    summaries and plots.
  - Added random-search tuning with search = "random" and n_evals, plus
    nested resampling support in tune() for outer-fold performance
    estimates of the model-selection procedure.
  - Hardened the fit/predict contract with clearer errors for missing
    predictor columns and unseen factor levels, stricter
    probability-output normalization, and broader learner contract
    coverage across the registry.
  - Added multiclass and weighted AUC support and clarified default
    evaluation behavior for binary versus multiclass classification.
  - Added list_learners() as a learner capability catalog and improved
    package metadata, citation, and repository scaffolding for release
    and paper preparation.
  - Removed the catboost learner backend from the registry and package
    metadata.
  - Kept lightgbm as a standard learner dependency available with
    funcml.

                        Changes in version 0.2.0                        

  - Added richer evaluation-centered resampling with plain holdout,
    grouped cross-validation, and time-aware rolling splits.
  - Added uncertainty summaries to evaluate() and compare_learners(),
    including fold-level standard errors and confidence intervals in
    summaries and plots.
  - Extended estimate() with configurable interval reporting, including
    bootstrap percentile intervals for average causal estimands.
  - Added random-search tuning with search = "random" and n_evals for
    budgeted hyperparameter search.
  - Added nested resampling to tune() via outer_resampling, so tuning
    can report unbiased outer-fold performance estimates for the
    selected workflow.
  - Hardened the fit/predict contract with clearer errors for missing
    predictor columns and unseen factor levels, plus stricter
    probability-output normalization.
  - Expanded the test suite with focused coverage for resampling,
    uncertainty, tuning, and prediction-contract behavior.

                        Changes in version 0.1.1                        

  - Vendored canonical interpretability implementations from vip, pdp,
    iml, and a minimal internal shapviz layer.
  - Replaced runtime vip and pdp dependencies with internal
    implementations while preserving the existing funcml entrypoints.
  - Added parity tests against sourced upstream reference code for
    permutation importance, PDP, ICE, ALE, Shapley values, and local
    surrogate explanations.
  - Switched local / local_model to an iml::LocalModel-style sparse
    local surrogate using glmnet and Gower weighting.