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rbart  

Bayesian Trees for Conditional Mean and Variance
View on CRAN: Click here


Download and install rbart package within the R console
Install from CRAN:
install.packages("rbart")

Install from Github:
library("remotes")
install_github("cran/rbart")

Install by package version:
library("remotes")
install_version("rbart", "1.0")



Attach the package and use:
library("rbart")
Maintained by
Robert McCulloch
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-01
Latest Update:
Description:
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, ). 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, ).
How to cite:
Robert McCulloch (2019). rbart: Bayesian Trees for Conditional Mean and Variance. R package version 1.0, https://cran.r-project.org/web/packages/rbart. Accessed 08 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:48), 1.0 (2019-08-01 11:20)
Other packages that cited rbart R package
View rbart citation profile
Other R packages that rbart depends, imports, suggests or enhances
Functions, R codes and Examples using the rbart R package
Some associated functions: hbartqqplot . plotFunctionDraws . predict.rbart . rbart . rbartModelMatrix . rbartonsimd . simdat . ucarprice . 
Some associated R codes: dotree.R . hbartqqplot.R . mbrt.R . plotFunctionDraws.R . predict.rbart.R . rbart.R . rbartModelMatrix.R .  Full rbart package functions and examples
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