dtGAP 0.0.2

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.

dtGAP 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.