NEWS

funcml 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.

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.

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.

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.

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.