"BARTBMA Bayesian Additive Regression Trees using Bayesian Model Averaging" (Hernandez B, Raftery A.E., Parnell A.C. (2018) <doi:10.1007/s1122201797671>) is an extension to the original BART sumoftrees model (Chipman et al 2010). BARTBMA 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 BARTBMA uses a greedy search for the best split points and variables when growing decision trees within each sumoftrees 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), BARTBMA uses a greedy implementation of Bayesian Model Averaging called Occam's Window which take a weighted average over multiple sumoftrees models to form its overall prediction. This means that only the set of sumoftrees for which there is high support from the data are saved to memory and used in the final model.
Package details 


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> 
License  GPL (>= 2) 
Version  1.0 
Package repository  View on CRAN 
Installation 
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