# Supervised Generalized Association Plots Based on Decision Trees

## Documentation for package ‘dtGAP’ version 0.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

add_data_type | Assigns a train/test indicator to a combined dataset
compare_dtGAP | Compare Multiple Decision Tree Models Side-by-Side
compute_tree | Compute Decision Tree Data for Plotting and Analysis
diabetes | Diabetes patient records.
draw_all | Draw Full Visualization: Decision Tree with Heatmap and Evaluation
dtGAP | Decision Tree Generalized Association Plots (dtGAP)
eval_tree | Evaluate Tree Model Predictions and Metrics
galaxy | Galaxy dataset for regression.
penguins | Data of three different species of penguins.
prepare_features | Prepare Features for Modeling
prepare_tree | Prepare Tree Plot Data for Visualization
Psychosis_Disorder | Psychosis Disorder Data
rf_dtGAP | Visualize a Single Tree from a Conditional Random Forest
rf_summary | Random Forest Ensemble Summary
save_dtGAP | Save dtGAP Visualization to File
scale_norm | Performs transformation on continuous variables.
sorted_mat | Sort Feature Matrix by Tree and Correlation Structure
test_covid | External test dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18.
train_covid | Training dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18. Containing NAs.
train_rf | Fit a Conditional Random Forest
train_tree | Fit a Decision Tree Model
wine | Results of a chemical analysis of wines grown in a specific area of Italy.
wine_quality_red | Red variant of the Portuguese "Vinho Verde" wine.