rbart: Bayesian Trees for Conditional Mean and Variance

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-devel-UCRT: rbart_1.0.zip, r-release: rbart_1.0.zip, r-oldrel: rbart_1.0.zip
macOS binaries: r-release (arm64): rbart_1.0.tgz, r-release (x86_64): rbart_1.0.tgz, r-oldrel: rbart_1.0.tgz


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