Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Timetoevent Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + s(x) E where functions f and s have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a technical description of the model <https://www.mcw.edu//media/MCW/Departments/Biostatistics/tr72.pdf?la=en>.
Package details 


Author  Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb] 
Maintainer  Rodney Sparapani <rsparapa@mcw.edu> 
License  GPL (>= 2) 
Version  1.3 
Package repository  View on CRAN 
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