## bartBMA: Bayesian Additive Regression Trees using Bayesian Model
Averaging

"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.

Version: |
1.0 |

Imports: |
Rcpp (≥ 1.0.0), mvnfast, Rdpack |

LinkingTo: |
Rcpp, RcppArmadillo, BH |

Published: |
2020-03-13 |

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 at tcd.ie> |

License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |

NeedsCompilation: |
yes |

Materials: |
README |

CRAN checks: |
bartBMA results |

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