funcml 0.7.1

• 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.

funcml 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.

funcml 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.

funcml 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.