pgbart v0.6.16


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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>.

Functions in pgbart

Name Description
pdpgbart Partial Dependence Plots for PGBART
pgbart_train Train Bayesian Additive Regression Trees Using PG Sampler or Gibbs/NH Sampler
pgbart_predict Make Predictions Using Bayesian Additive Regression Trees
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Type Package
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

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