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: |
2.6 |
| Depends: |
R (≥ 2.10), nlme, nnet, survival |
| Imports: |
Rcpp (≥ 0.12.3), parallel, tools |
| LinkingTo: |
Rcpp |
| Suggests: |
MASS, knitr, rmarkdown, sbart |
| Published: |
2019-10-02 |
| 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 |