A model of the form Y = f(x) + s(x) Z is fit where functions f and s are modeled with ensembles of trees and Z is standard normal. This model is developed in the paper 'Heteroscedastic BART Via Multiplicative Regression Trees' (Pratola, Chipman, George, and McCulloch, 2019, <arXiv:1709.07542v2>). BART refers to Bayesian Additive Regression Trees. See the R-package 'BART'. The predictor vector x may be high dimensional. A Markov Chain Monte Carlo (MCMC) algorithm provides Bayesian posterior uncertainty for both f and s. The MCMC uses the recent innovations in Efficient Metropolis–Hastings proposal mechanisms for Bayesian regression tree models (Pratola, 2015, Bayesian Analysis, <doi:10.1214/16-BA999>).
| Version: | 1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 0.12.3) |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, MASS, nnet |
| Published: | 2019-08-01 |
| Author: | Robert McCulloch [aut, cre, cph], Matthew Pratola [aut, cph], Hugh Chipman [aut, cph] |
| Maintainer: | Robert McCulloch <robert.e.mcculloch at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| CRAN checks: | rbart results |
| Reference manual: | rbart.pdf |
| Package source: | rbart_1.0.tar.gz |
| Windows binaries: | r-devel: rbart_1.0.zip, r-release: rbart_1.0.zip, r-oldrel: rbart_1.0.zip |
| macOS binaries: | r-release: rbart_1.0.tgz, r-oldrel: rbart_1.0.tgz |
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