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: |
2.9.9 |
| Depends: |
R (≥ 3.6), nlme, survival |
| Imports: |
Rcpp (≥ 0.12.3), parallel, tools |
| LinkingTo: |
Rcpp |
| Suggests: |
MASS, knitr, rmarkdown |
| Published: |
2024-06-21 |
| DOI: |
10.32614/CRAN.package.BART |
| Author: |
Robert McCulloch [aut],
Rodney Sparapani [aut, cre],
Robert Gramacy [ctb],
Matthew Pratola [ctb],
Charles Spanbauer [ctb],
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 |
| Citation: |
BART citation info |
| Materials: |
NEWS |
| In views: |
Bayesian, MachineLearning |
| CRAN checks: |
BART results [issues need fixing before 2026-02-25] |