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>.

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Package
pgbart
Type
Package
Title
Bayesian Additive Regression Trees Using Particle Gibbs Sampler and Gibbs/Metropolis-Hastings Sampler
Version
0.6.16
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
Description
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, .
Depends
R (>= 3.2.2)
Imports
BayesTree (>= 0.3-1.4)
License
GPL (>= 2)
Encoding
UTF-8
NeedsCompilation
yes
Packaged
2019-03-13 06:24:58 UTC; Apple
Repository
CRAN
Date/Publication
2019-03-13 06:40:03 UTC

install.packages('pgbart')

0.6.16

10 days ago

Pingyu Wang

GPL (>= 2)

Depends on

R (>= 3.2.2)

Imports

BayesTree (>= 0.3-1.4)

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