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bartMachine (version 1.4)

Bayesian Additive Regression Trees

Description

An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.

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Version

Install

install.packages('bartMachine')

Monthly Downloads

6,031

Version

1.4

License

GPL-3

Maintainer

Adam Kapelner

Last Published

January 17th, 2026

Functions in bartMachine (1.4)

interaction_investigator

Explore Pairwise Interactions in BART Model
get_var_props_over_chain

Get the Variable Inclusion Proportions
cov_importance_test

Importance Test for Covariate(s) of Interest
dummify_data

Dummify Design Matrix
get_var_counts_over_chain

Get the Variable Inclusion Counts
investigate_var_importance

Explore Variable Inclusion Proportions in BART Model
predict.bartMachine

Make a prediction on data using a BART object
node_prediction_training_data_indices

Gets node predictions indices of the training data for new data.
plot_y_vs_yhat

Plot the fitted Versus Actual Response
linearity_test

Test of Linearity
get_projection_weights

Gets Training Sample Projection / Weights
pd_plot

Partial Dependence Plot
plot_convergence_diagnostics

Plot Convergence Diagnostics
print.bartMachine

Summarizes information about a bartMachine object.
predict_bartMachineArr

Make a prediction on data using a BART array object
rmse_by_num_trees

Assess the Out-of-sample RMSE by Number of Trees
set_bart_machine_num_cores

Set the Number of Cores for BART
var_selection_by_permute

Perform Variable Selection using Three Threshold-based Procedures
summary.bartMachine

Summarizes information about a bartMachine object.
var_selection_by_permute_cv

Perform Variable Selection Using Cross-validation Procedure
k_fold_cv

Estimate Out-of-sample Error with K-fold Cross validation
bartMachineArr

Create an array of BART models for the same data.
bartMachineCV

Build BART-CV
bartMachine

Build a BART Model
bartMachine-package

bartMachine: Bayesian Additive Regression Trees
bart_machine_get_posterior

Get Full Posterior Distribution
bart_machine_num_cores

Get Number of Cores Used by BART
benchmark_datasets

benchmark_datasets
bart_predict_for_test_data

Predict for Test Data with Known Outcomes
calc_credible_intervals

Calculate Credible Intervals
automobile

Data concerning automobile prices.
check_bart_error_assumptions

Check BART Error Assumptions
calc_prediction_intervals

Calculate Prediction Intervals
extract_raw_node_data

Gets Raw Node data
get_sigsqs

Get Posterior Error Variance Estimates