Package: rbart
Type: Package
Title: Bayesian Trees for Conditional Mean and Variance
Version: 1.0
Date: 2019-07-28
Authors@R: c(person('Robert', 'McCulloch', role=c('aut','cre','cph'),email='robert.e.mcculloch@gmail.com'),
  person('Matthew', 'Pratola', role=c('aut','cph')), person('Hugh','Chipman',role=c('aut','cph')))
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, <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>).
License: GPL (>= 2)
Depends: R (>= 2.10)
Imports: Rcpp (>= 0.12.3)
Suggests: knitr, rmarkdown, MASS, nnet
LinkingTo: Rcpp
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2019-07-28 16:17:05 UTC; rob
Author: Robert McCulloch [aut, cre, cph],
  Matthew Pratola [aut, cph],
  Hugh Chipman [aut, cph]
Maintainer: Robert McCulloch <robert.e.mcculloch@gmail.com>
Repository: CRAN
Date/Publication: 2019-08-01 09:20:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 04:44:39 UTC; windows
Archs: i386, x64
