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 on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.

Version: 2.5
Depends: R (≥ 2.10), nlme, nnet, survival
Imports: Rcpp (≥ 0.12.3), parallel, tools
LinkingTo: Rcpp
Suggests: MASS, knitr, rmarkdown, sbart
Published: 2019-06-17
Author: Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [aut], Charles Spanbauer [aut], Matthew Pratola [aut], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]
Maintainer: Rodney Sparapani <rsparapa at mcw.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
In views: Bayesian, MachineLearning
CRAN checks: BART results

Downloads:

Reference manual: BART.pdf
Vignettes: The BART R package
Package source: BART_2.5.tar.gz
Windows binaries: r-devel: BART_2.5.zip, r-release: BART_2.5.zip, r-oldrel: BART_2.5.zip
OS X binaries: r-release: BART_2.5.tgz, r-oldrel: BART_2.5.tgz
Old sources: BART archive

Reverse dependencies:

Reverse imports: borrowr
Reverse suggests: MachineShop, StratifiedMedicine

Linking:

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