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, <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, <doi:10.1214/09-aoas285>.

Version: 0.6.15
Depends: R (≥ 3.2.2)
Imports: BayesTree (≥ 0.3-1.4)
Published: 2018-11-13
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

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Reference manual: pgbart.pdf
Package source: pgbart_0.6.15.tar.gz
Windows binaries: r-devel: pgbart_0.6.15.zip, r-release: pgbart_0.6.15.zip, r-oldrel: pgbart_0.6.15.zip
OS X binaries: r-release: pgbart_0.6.15.tgz, r-oldrel: pgbart_0.6.15.tgz
Old sources: pgbart archive

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