pgbart: Bayesian Additive Regression Trees Using Particle Gibbs Sampler and Gibbs/Metropolis-Hastings Sampler

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, <> and Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298, <>.

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>
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:, r-release:, r-oldrel:
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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