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, <doi:10.1214/09-aoas285>.

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Package details

AuthorPingyu Wang [aut, cre], Dai Feng [aut], Yang Bai [aut], Qiuyue Shi [aut], Zhicheng Zhao [aut], Fei Su [aut], Hugh Chipman [aut], Robert McCulloch [aut]
MaintainerPingyu Wang <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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pgbart documentation built on May 2, 2019, 8:42 a.m.