The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented to fit Bayesian additive regression tree model. Construction of the model (training) and prediction for a new data set (testing) can be separated. Our reference papers are: Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C], Artificial Intelligence and Statistics. 2015: 553-561, <http://proceedings.mlr.press/v38/lakshminarayanan15.pdf> and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <https://doi.org/10.1214/09-aoas285>.
| Version: | 0.6.12 |
| Depends: | R (≥ 3.2.2) |
| Imports: | BayesTree (≥ 0.3-1.4) |
| Published: | 2018-07-06 |
| Author: | Pingyu Wang [aut, cre], Dai Feng [aut], Yang Bai [aut], Qiuyue Shi [aut], Zhicheng Zhao [aut], Fei Su [aut], Hugh Chipman [aut], Robert McCulloch [aut] |
| Maintainer: | Pingyu Wang <applewangpingyu at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | pgbart citation info |
| CRAN checks: | pgbart results |
| Reference manual: | pgbart.pdf |
| Package source: | pgbart_0.6.12.tar.gz |
| Windows binaries: | r-devel: pgbart_0.6.12.zip, r-release: pgbart_0.6.12.zip, r-oldrel: pgbart_0.6.12.zip |
| OS X binaries: | r-release: pgbart_0.6.12.tgz, r-oldrel: pgbart_0.6.12.tgz |
| Old sources: | pgbart archive |
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