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.
funcml 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.
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.