Package: bartBMA
Type: Package
Title: Bayesian Additive Regression Trees using Bayesian Model
        Averaging
Version: 1.0
Date: 2020-02-05
Author: Belinda Hernandez [aut, cre]
    Adrian E. Raftery [aut]
    Stephen R Pennington [aut]
    Andrew C. Parnell [aut]
    Eoghan O'Neill [ctb]
Maintainer: Belinda Hernandez <HERNANDB@tcd.ie>
Description: "BART-BMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) <doi:10.1007/s11222-017-9767-1>) is an extension to the original BART sum-of-trees model (Chipman et al 2010). BART-BMA differs to the original BART model in two main aspects in order to implement a greedy model which 
  will be computationally feasible for high dimensional data. Firstly BART-BMA uses a greedy search for the best split points and variables when growing decision trees within each sum-of-trees 
  model. This means trees are only grown based on the most predictive set of split rules. Also rather than using Markov chain Monte Carlo (MCMC), BART-BMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window 
  which take a weighted average over multiple sum-of-trees models to form its overall prediction. This means that only the set of sum-of-trees for which there is high support from the data
  are saved to memory and used in the final model.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.0), mvnfast, Rdpack
RdMacros: Rdpack
LinkingTo: Rcpp, RcppArmadillo, BH
RoxygenNote: 7.0.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-03-05 17:24:23 UTC; HERNANDB
Repository: CRAN
Date/Publication: 2020-03-13 11:50:05 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-05-16 09:48:20 UTC; windows
Archs: i386, x64
