Package: pgbart
Type: Package
Title: Bayesian Additive Regression Trees Using Particle Gibbs Sampler
        and Gibbs/Metropolis-Hastings Sampler
Version: 0.6.15
Authors@R: c(person("Pingyu","Wang", role = c("aut", "cre"),email = "applewangpingyu@gmail.com"),
            person("Dai","Feng", role = "aut",email = "dai_feng@merck.com"),
            person("Yang","Bai",  role = "aut",email = "by0132@live.cn"),
            person("Qiuyue","Shi", role = "aut",email = "shiqiuyue@bupt.edu.cn"),
            person("Zhicheng","Zhao", role = "aut",email = "zhaozc@bupt.edu.cn"),
            person("Fei","Su", role = "aut",email = "sufei@bupt.edu.cn"),
            person("Hugh","Chipman", role = "aut",email = "hugh.chipman@gmail.com"),
            person("Robert","McCulloch", role = "aut",email = "robert.e.mcculloch@gmail.com"))
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@gmail.com>
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, 
             <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>.
Depends: R (>= 3.2.2)
Imports: BayesTree (>= 0.3-1.4)
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2018-11-13 12:28:13 UTC; Apple
Repository: CRAN
Date/Publication: 2018-11-13 12:50:03 UTC
