Package: bartMachine 1.4.3

bartMachine: Bayesian Additive Regression Trees

Fits Bayesian additive regression trees (BART), a Bayesian nonparametric machine-learning method, for regression and classification. Provides posterior inference, credible and prediction intervals, variable selection, interaction analysis, partial-dependence plots, cross-validation, and optional graphics processing unit (GPU) acceleration through TornadoVM.

Authors:Adam Kapelner [aut, cre, cph], Justin Bleich [aut, cph]

bartMachine_1.4.3.tar.gz
bartMachine_1.4.3.zip(r-4.7-any)bartMachine_1.4.3.zip(r-4.6-any)bartMachine_1.4.3.zip(r-4.5-any)
bartMachine_1.4.3.tgz(r-4.6-any)bartMachine_1.4.3.tgz(r-4.5-any)
bartMachine_1.4.3.tar.gz(r-4.7-any)bartMachine_1.4.3.tar.gz(r-4.6-any)
bartMachine_1.4.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bartMachine/json (API)

# Install 'bartMachine' in R:

install.packages('bartMachine', repos = c('https://kapelner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kapelner/bartmachine/issues

Pkgdown/docs site:https://kapelner.github.io

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

Datasets:

ankara - Benchmark_datasets
automobile - Data concerning automobile prices.
baseball - Benchmark_datasets
boston - Benchmark_datasets
compactiv - Benchmark_datasets
ozone - Benchmark_datasets
pole - Benchmark_datasets
triazine - Benchmark_datasets
wine.red - Benchmark_datasets
wine.white - Benchmark_datasets

On CRAN:

bayesian additive regression trees bart bayesian machine learning bayesian nonparametrics machine learning ensemble learning decision trees regression classification posterior inference mcmc variable selection uncertainty quantification interpretable machine learning partial dependence cross-validation gpu acceleration bayesian-additive-regression-trees bayesian-machine-learning bayesian-nonparametrics bayesian-statistics decision-trees ensemble-learning gpu-acceleration interpretable-machine-learning machine-learning partial-dependence posterior-inference uncertainty-quantification variable-selection openjdk

11.07 score 67 stars 3 packages 327 scripts 3.9k downloads 12 mentions 31 exports 38 dependencies

Last updated from:ca90cdd752. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	154
source / vignettes	OK	226
linux-release	OK	158
macos-release	OK	118
macos-oldrel	OK	139
windows-devel	OK	104
windows-release	OK	122
windows-oldrel	OK	100
wasm-release	OK	127

Exports:bart_machine_get_posterior bart_machine_num_cores bart_predict_for_test_data bartMachine bartMachineArr bartMachineCV build_bart_machine build_bart_machine_cv calc_credible_intervals calc_prediction_intervals check_bart_error_assumptions cov_importance_test dummify_data extract_raw_node_data get_projection_weights get_sigsqs get_var_counts_over_chain get_var_props_over_chain interaction_investigator investigate_var_importance k_fold_cv linearity_test node_prediction_training_data_indices pd_plot plot_convergence_diagnostics plot_y_vs_yhat predict_bartMachineArr rmse_by_num_trees set_bart_machine_num_cores var_selection_by_permute var_selection_by_permute_cv

Dependencies:backports bartMachineJARs checkmate cli codetools cpp11 digest doRNG farver foreach ggplot2 glue gtable isoband iterators itertools labeling lattice lifecycle Matrix matrixStats missForest R6 randomForest ranger rbibutils RColorBrewer Rcpp RcppEigen Rdpack rJava rlang rngtools S7 scales vctrs viridisLite withr

bartMachine

Rendered frombartMachine.Rnwusingutils::Sweave

Last update: 2026-01-13
Started: 2014-11-24

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Data concerning automobile prices.	automobile
Get Full Posterior Distribution	bart_machine_get_posterior
Get Number of Cores Used by BART	bart_machine_num_cores
Predict for Test Data with Known Outcomes	bart_predict_for_test_data
Build a BART Model	bartMachine build_bart_machine
Create an array of BART models for the same data.	bartMachineArr
Build BART-CV	bartMachineCV build_bart_machine_cv
benchmark_datasets	ankara baseball benchmark_datasets boston compactiv ozone pole triazine wine.red wine.white
Calculate Credible Intervals	calc_credible_intervals
Calculate Prediction Intervals	calc_prediction_intervals
Check BART Error Assumptions	check_bart_error_assumptions
Importance Test for Covariate(s) of Interest	cov_importance_test
Dummify Design Matrix	dummify_data
Gets Raw Node data	extract_raw_node_data
Gets Training Sample Projection / Weights	get_projection_weights
Get Posterior Error Variance Estimates	get_sigsqs
Get the Variable Inclusion Counts	get_var_counts_over_chain
Get the Variable Inclusion Proportions	get_var_props_over_chain
Explore Pairwise Interactions in BART Model	interaction_investigator
Explore Variable Inclusion Proportions in BART Model	investigate_var_importance
Estimate Out-of-sample Error with K-fold Cross validation	k_fold_cv
Test of Linearity	linearity_test
Gets node predictions indices of the training data for new data.	node_prediction_training_data_indices
Partial Dependence Plot	pd_plot
Plot Convergence Diagnostics	plot_convergence_diagnostics
Plot the fitted Versus Actual Response	plot_y_vs_yhat
Make a prediction on data using a BART array object	predict_bartMachineArr
Make a prediction on data using a BART object	predict.bartMachine
Summarizes information about a 'bartMachine' object.	print.bartMachine
Assess the Out-of-sample RMSE by Number of Trees	rmse_by_num_trees
Set the Number of Cores for BART	set_bart_machine_num_cores
Summarizes information about a 'bartMachine' object.	summary.bartMachine
Perform Variable Selection using Three Threshold-based Procedures	var_selection_by_permute
Perform Variable Selection Using Cross-validation Procedure	var_selection_by_permute_cv