Changes in version 2.4.2 (2026-11-12)                  

First release under the name PsyML, prepared for CRAN.

  - The package was renamed from easyML (version 2.4.2) to PsyML.
  - The package is organized around three approaches, each run by one
    function: supervised learning (supervised_ml()), unsupervised
    learning (unsupervised_ml()) and causal inference (causal_ml()). The
    package help page (?PsyML), the new vignette "Three Approaches" and
    the "Other supervised learning", "Other unsupervised learning" and
    "Other causal inference" links in each help page show which
    functions belong to each approach.
  - The reinforcement learning module (reinforcement_ml() and its
    methods, bandits, MDP and tabular agents, which depended on the
    contextual package) was removed.
  - Variable importance no longer uses the archived vip package:
    get_importance() now uses model-specific measures (impurity for
    ranger and rpart, gain for xgboost, absolute coefficients for
    glmnet, absolute z/t statistics for glm/lm) and falls back to
    permutation importance implemented in the package.
  - SHAP values are computed with kernelshap (Suggests) instead of the
    archived fastshap package; shapviz (Suggests) is still used for
    plots when installed.
  - All documentation, the vignette, the README and the condition
    messages (errors, warnings, messages) are now in English. Console
    progress output of the pipelines is still in Spanish.
  - seed arguments now default to NULL: the random number generator is
    only seeded when a seed is supplied. supervised_ml() uses 2 cores by
    default (n_cores = 2).
  - Nothing is written to disk by default: export_verbose_txt(),
    export_verbose_json(), generate_report_from_json(),
    supervised_ml_export(), save_all_plots(), save_unsupervised_plots()
    and generate_report_with_ai() require an explicit path.
  - explain_with_ai() and generate_report_with_ai() no longer include a
    shared API key; they read the key from api_key or from the
    environment variable PSYML_API_KEY. explain_with_ai() gains a
    verbose argument.
  - predict.unsupervisedml() now takes object as its first argument, and
    Gaussian mixture clustering no longer attaches mclust.
  - cluster_profile() gains a verbose argument.
  - Imports were reduced to the packages actually used (the tidyverse
    and tidymodels meta-packages are no longer imported).

  - fit_models_cv(): in binary classification the cross-validated PR-AUC
    was computed for the negative class, because tune::fit_resamples()
    passes the first probability column to the metrics and pr_auc did
    not follow the event_level. With imbalanced classes this gave
    implausible values (e.g. .999 with 4% positives versus a true .786).
    It is now recomputed per fold with the probability of the positive
    class. ROC-AUC was not affected.

                        Changes in version 2.4.1                        

Fixes found while running supervised_ml() on data with strongly
imbalanced classes (4% positives) and highly collinear predictors.

  - compare_models(): supervised_ml() with a single model failed when
    computing the average ranking.
  - calculate_shap(): predictors removed by preprocessing (high
    correlation, VIF, zero variance) appeared in the SHAP importance
    with value 0; they are now excluded and reported in $removed_vars.
  - calculate_shap(): new $collinear_vars with the predictors used that
    have VIF > 5, and a note that the split of SHAP values among nearly
    redundant predictors should not be interpreted variable by variable.
  - calculate_shap(): in classification the SHAP sample is stratified
    with the same number of cases per class ($sample_design).
  - Optimal threshold: it is now chosen with out-of-fold predictions on
    the training set and evaluated on the test set
    ($threshold_optimization$source,
    $threshold_optimization$test_metrics). Previously it was chosen and
    evaluated on the same test predictions.