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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