Package: bartMachine 1.4.1.1

bartMachine: Bayesian Additive Regression Trees

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

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

bartMachine_1.4.1.1.tar.gz
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bartMachine.pdf |bartMachine.html✨
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

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

On CRAN:

Conda:

openjdk

11.03 score 64 stars 3 packages 320 scripts 5.8k downloads 12 mentions 31 exports 38 dependencies

Last updated from:a3ac6a31ab. Checks:9 OK. Indexed: yes.

TargetResultTotal timeArtifact
linux-devel-x86_64OK145
source / vignettesOK184
linux-release-x86_64OK147
macos-devel-arm64OK93
macos-release-arm64OK176
windows-develOK102
windows-releaseOK109
windows-oldrelOK111
wasm-releaseOK129

Exports:bart_machine_get_posteriorbart_machine_num_coresbart_predict_for_test_databartMachinebartMachineArrbartMachineCVbuild_bart_machinebuild_bart_machine_cvcalc_credible_intervalscalc_prediction_intervalscheck_bart_error_assumptionscov_importance_testdummify_dataextract_raw_node_dataget_projection_weightsget_sigsqsget_var_counts_over_chainget_var_props_over_chaininteraction_investigatorinvestigate_var_importancek_fold_cvlinearity_testnode_prediction_training_data_indicespd_plotplot_convergence_diagnosticsplot_y_vs_yhatpredict_bartMachineArrrmse_by_num_treesset_bart_machine_num_coresvar_selection_by_permutevar_selection_by_permute_cv

Dependencies:backportsbartMachineJARscheckmateclicodetoolscpp11digestdoRNGfarverforeachggplot2gluegtableisobanditeratorsitertoolslabelinglatticelifecycleMatrixmatrixStatsmissForestR6randomForestrangerrbibutilsRColorBrewerRcppRcppEigenRdpackrJavarlangrngtoolsS7scalesvctrsviridisLitewithr

bartMachine

Rendered frombartMachine.Rnwusingutils::Sweaveon Feb 19 2026.

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

Readme and manuals

Help Manual

Help pageTopics
Data concerning automobile prices.automobile
Get Full Posterior Distributionbart_machine_get_posterior
Get Number of Cores Used by BARTbart_machine_num_cores
Predict for Test Data with Known Outcomesbart_predict_for_test_data
Build a BART ModelbartMachine build_bart_machine
Create an array of BART models for the same data.bartMachineArr
Build BART-CVbartMachineCV build_bart_machine_cv
benchmark_datasetsankara baseball benchmark_datasets boston compactiv ozone pole triazine wine.red wine.white
Calculate Credible Intervalscalc_credible_intervals
Calculate Prediction Intervalscalc_prediction_intervals
Check BART Error Assumptionscheck_bart_error_assumptions
Importance Test for Covariate(s) of Interestcov_importance_test
Dummify Design Matrixdummify_data
Gets Raw Node dataextract_raw_node_data
Gets Training Sample Projection / Weightsget_projection_weights
Get Posterior Error Variance Estimatesget_sigsqs
Get the Variable Inclusion Countsget_var_counts_over_chain
Get the Variable Inclusion Proportionsget_var_props_over_chain
Explore Pairwise Interactions in BART Modelinteraction_investigator
Explore Variable Inclusion Proportions in BART Modelinvestigate_var_importance
Estimate Out-of-sample Error with K-fold Cross validationk_fold_cv
Test of Linearitylinearity_test
Gets node predictions indices of the training data for new data.node_prediction_training_data_indices
Partial Dependence Plotpd_plot
Plot Convergence Diagnosticsplot_convergence_diagnostics
Plot the fitted Versus Actual Responseplot_y_vs_yhat
Make a prediction on data using a BART array objectpredict_bartMachineArr
Make a prediction on data using a BART objectpredict.bartMachine
Summarizes information about a 'bartMachine' object.print.bartMachine
Assess the Out-of-sample RMSE by Number of Treesrmse_by_num_trees
Set the Number of Cores for BARTset_bart_machine_num_cores
Summarizes information about a 'bartMachine' object.summary.bartMachine
Perform Variable Selection using Three Threshold-based Proceduresvar_selection_by_permute
Perform Variable Selection Using Cross-validation Procedurevar_selection_by_permute_cv
Image

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