BART: Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Version: 2.9.4
Depends: R (≥ 2.10), nlme, nnet, survival
Imports: Rcpp (≥ 0.12.3), parallel, tools
LinkingTo: Rcpp
Suggests: MASS, knitr, rmarkdown
Published: 2023-03-25
Author: Robert McCulloch [aut], Rodney Sparapani [aut, cre], Charles Spanbauer [aut], Robert Gramacy [aut], Matthew Pratola [aut], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]
Maintainer: Rodney Sparapani <rsparapa at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: BART citation info
Materials: NEWS
In views: Bayesian, MachineLearning
CRAN checks: BART results


Reference manual: BART.pdf
Vignettes: The BART R package


Package source: BART_2.9.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BART_2.9.4.tgz, r-oldrel (arm64): BART_2.9.4.tgz, r-release (x86_64): BART_2.9.4.tgz, r-oldrel (x86_64): BART_2.9.4.tgz
Old sources: BART archive

Reverse dependencies:

Reverse depends: cjbart
Reverse imports: borrowr, CIMTx, paths, RCTrep, riAFTBART, SAMTx
Reverse suggests: bark, condvis2, CRE, familiar, MachineShop, StratifiedMedicine, tidytreatment


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