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: | 2.0 |
Depends: | R (≥ 2.10), survival, nnet |
Imports: | Rcpp (≥ 0.12.3), parallel, tools |
LinkingTo: | Rcpp |
Suggests: | knitr, rmarkdown, sbart, MASS |
Published: | 2018-11-18 |
Author: | Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [aut], Charles Spanbauer [aut], Matthew Pratola [aut], Bill Venables [ctb], Brian Ripley [ctb] |
Maintainer: | Rodney Sparapani <rsparapa at mcw.edu> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
SystemRequirements: | C++11 |
Materials: | NEWS |
In views: | Bayesian, MachineLearning |
CRAN checks: | BART results |
Reference manual: | BART.pdf |
Vignettes: |
wbart, BART for Numeric Outcomes Binary and categorical outcomes with BART Efficient computing with BART Continuous outcomes with BART: Part 1 Continuous outcomes with BART: Part 2 Time-to-event outcomes with BART |
Package source: | BART_2.0.tar.gz |
Windows binaries: | r-devel: BART_2.0.zip, r-release: BART_2.0.zip, r-oldrel: BART_2.0.zip |
OS X binaries: | r-release: BART_2.0.tgz, r-oldrel: BART_2.0.tgz |
Old sources: | BART archive |
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