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An R-Java Bayesian Additive Regression Trees implementation (BART) Software for Supervised Statistical Learning

Copyright (C) 2022 Adam Kapelner
Department of Mathematics, Queens College, City University of New York & Justin Bleich Department of Statistics, The Wharton School of the University of Pennsylvania

This is a Java implementation of the algorithm found in Chipman, George, & McCulloch BART: Bayesian Additive Regressive Trees. The Annals of Applied Statistics. 2010 4(1): 266-298 as well as many other features.

Recent News


v1.3.1 is released and fixed bugs in v1.3 due to something I can't figure out in the fastutil library. Please don't use v1.3 --- upgrade to v1.3.1 or downgrade to v1.2.7


v1.3 is released (and should be on CRAN soon) which comes from some nice speedups. Note it is not backwards compatible with previous versions. To use previous versions' models, install a previous version manually e.g. via install.packages("", repos=NULL, type="source").

The Paper

For a vignette describing the BART model and bartMachine's features, see our JSS paper.

The Manual

See the manual for detailed information about the package's functions and parameters.

Setup Instructions

To install the bartMachine package in R, you first need to install Java and rJava and configure your computer, then you can install the package from CRAN or compile from source.

Install Java JDK (not the JRE)

Download the latest Java JDK and install it properly. (Java 7 or above is required for v1.2.x and Java 8 or above is required for v1.3 and above). bartMachine requires rJava which requires the JDK; you cannot just have a JRE!

Install rJava

Use install.packages("rJava") within R. If you experience errors, make sure your JAVA_HOME system variable is set to the root of your java installation (on a windows machine that would look something like C:\Program Files\Java\jdk-13.0.2). Also try running R CMD javareconf from the command line. On ubuntu, you should run sudo apt-get install r-cran-rjava to install from the command prompt. If you still have errors, you are not alone! rJava is tough to install and idiosyncratic across different platforms. Google your error message! The majority of issues have been resolved on Q&A forums.

Install bartMachine via CRAN

Use install.packages("bartMachine") within R and then options(java.parameters = "-Xmx2500m") to set a larger amount of RAM than the default of 500MB and then library(bartMachine).

Install bartMachine via compilation from source

Due to CRAN limitations, we cannot release bartMachine for Java >7. Thus it is recommended to install bartMachine from source. This is recommended as you will get the benefits of Java 8-14 as well as the latest release of bartMachine if it's not on CRAN yet (see changelog.

  1. Make sure you have git properly installed.

  2. Run git clone from your command line and navigate into the cloned project directory via cd bartMachine.

  3. Make sure you have a Java JDK installed properly. Then make sure the bin directory is an element in the PATH variable (on a windows machine it would look something like C:\Program Files\Java\jdk-13.0.2\bin). We also recommend making a system variable JAVA_HOME pointing to the directory (save \bin).

  4. Make sure you have apache ant installed properly. Make sure you add the bin directory for ant to your system PATH variable (on a windows machine it would be something like C:\Program Files (x86)\apache-ant-1.10.8\bin). We also recommend making a system variable ANT_HOME pointing to the directory (save \bin).

  5. Compile the JAVA source code into a JAR using ant. You should see a compilation record and then BUILD SUCCESSFUL and a total time.

  6. Now you can install the package into R using R CMD INSTALL bartMachine. On Windows systems, this may fail because it expects multiple architectures. This can be corrected by running R CMD INSTALL --no-multiarch bartMachine (I haven't seen this issue in years though). This may also fail if you don't have the required packages installed (run install.packages("bartMachineJARs") and install.packages("missForest")). Upon successful installation, the last line of the output should read DONE (bartMachine). In R, you can now run library(bartMachine) and start using the package normally.


We thank Ed George, Abba Krieger, Shene Jensen and Richard Berk for helpful discussions. We thank Matt Olson for pointing out an important memory issue. We thank JProfiler for profiling the code which allowed us to create a lean implementation.


An R-Java Bayesian Additive Regression Trees implementation







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