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>.

Getting started

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 <applewangpingyu@gmail.com>
LicenseGPL (>= 2)
Version0.6.16
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("pgbart")

Try the pgbart package in your browser

Any scripts or data that you put into this service are public.

pgbart documentation built on May 2, 2019, 8:42 a.m.