Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event 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) + sd(x) E where functions f and sd 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 complete description of the model at <doi:10.1111/biom.13857>.
|Author||Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb]|
|Maintainer||Rodney Sparapani <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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