Changes in version 1.0.0                        

First stable CRAN release. The Hastie workflow in R.

Core workflow

  - Four-verb grammar: ml_split(), ml_fit(), ml_evaluate(), ml_assess().
    The evaluate/assess boundary prevents data leakage by separating
    iterative model selection from final generalization estimates.
  - Three-way data splitting (train/valid/test) with automatic
    stratification for classification targets.
  - Mandatory reproducibility: seeds are auto-generated and stored when
    not provided explicitly.
  - Per-fold preprocessing: encoding and scaling fit on training folds
    only.

Algorithms

  - 13 algorithms: logistic regression, linear regression (ridge),
    decision tree, random forest, extra trees, gradient boosting,
    histogram gradient boosting, XGBoost, KNN, naive Bayes, SVM, elastic
    net, AdaBoost.
  - Optional backends via Suggests: xgboost, ranger, glmnet, e1071,
    kknn, naivebayes, rpart. Without these, algorithms fall back to the
    optional Rust engine or report as unavailable.

Additional verbs

  - ml_screen() for rapid algorithm comparison across all available
    backends.
  - ml_tune() for hyperparameter tuning with random search and
    cross-validation.
  - ml_stack() for model ensembling via out-of-fold stacking.
  - ml_drift() for data drift detection (KS test).
  - ml_shelf() for model staleness monitoring.
  - ml_explain() for feature importance (impurity-based and
    coefficient-based).
  - ml_calibrate() for probability calibration (Platt scaling).
  - ml_validate() for pass/fail gating against user-defined rules.
  - ml_profile() for dataset profiling.
  - ml_save() / ml_load() for model persistence in .mlr format.

Rust backend

  - Optional compiled Rust engine via extendr bindings. Detected at
    install time by configure; no Rust required for a fully functional
    installation.

                        Changes in version 0.2.0                        

Changed

  - ml_assess() terminal constraint now enforced per-partition via
    content-addressed fingerprinting. Serialization and deepcopy
    bypasses are closed.

New

  - ml_cv(), ml_cv_temporal(), ml_cv_group() for cross-validation.
  - ml_verify() for post-fit model verification.
  - ml_prepare() for explicit preprocessing.
  - Content-addressed provenance registry (rlang::hash fingerprinting).
  - Cross-verb provenance checks (train/test from same split).

Fixed

  - Per-holdout assess enforcement: ml_assess() now rejects a second
    call on the same test partition regardless of which model calls it.
  - Fixed ml_prepare() return value extraction (X and norm fields).

                 Changes in version 0.1.2 (2026-03-19)                  

New

  - ml_cv(), ml_cv_temporal(), ml_cv_group() for cross-validation.
  - ml_verify() for post-fit model verification.
  - ml_prepare() for explicit preprocessing.
  - Content-addressed provenance registry (rlang::hash fingerprinting).
  - Cross-verb provenance checks (train/test from same split).

Fixed

  - Per-holdout assess enforcement: ml_assess() now rejects a second
    call on the same test partition regardless of which model calls it.
    The provenance registry tracks spent holdouts via content-addressed
    fingerprinting.
  - Fixed ml_prepare() return value extraction (X and norm fields).