Changes in version 0.0.2 (2026-02-18)                  

New Features

  - save_dtGAP(): Export dtGAP visualizations to PNG, PDF, or SVG files
    with customizable dimensions and resolution.
  - select_vars parameter in dtGAP(): Display-only variable filtering
    for heatmap panels while the tree is trained on all variables.
  - fit and user_var_imp parameters in dtGAP(): Supply a pre-trained
    tree (rpart, party, or caret) directly, with automatic model
    detection and optional user-provided variable importance.
  - interactive parameter in dtGAP(): Launch a Shiny-based interactive
    heatmap viewer via InteractiveComplexHeatmap.
  - compare_dtGAP(): Compare two or more tree models side-by-side on a
    single wide canvas.
  - Random forest extension via partykit::cforest:
      - train_rf(): Train a conditional random forest and extract
        variable importance.
      - rf_summary(): Ensemble-level summary with variable importance
        barplot and representative tree identification.
      - rf_dtGAP(): Visualize any individual tree from the forest using
        the full dtGAP pipeline.

Bug Fixes

  - Fix formatC() error in prepare_tree() for cforest trees that lack
    numeric p-values.

Documentation

  - Updated vignette with usage examples for all new features.
  - Updated README with new feature descriptions and code examples.

                        Changes in version 0.0.1                        

  - Initial release.
  - Core dtGAP() function for supervised decision-tree visualization
    using the GAP framework.
  - Support for rpart, party, C5.0, and caret tree models.
  - Confusion matrix maps, decision-tree matrix maps, predicted class
    membership maps, and evaluation panels.
  - Row and column proximity with seriation support.
  - Classification and regression tasks.
  - Seven built-in datasets: Psychosis_Disorder, penguins, wine,
    diabetes, train_covid/test_covid, wine_quality_red, galaxy.