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 |
Documentation:
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