Nonparametric Failure Time Bayesian Additive Regression Trees

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) + 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>.


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install.packages("nftbart")

1.1 by Rodney Sparapani, a month ago


Browse source code at https://github.com/cran/nftbart


Authors: Rodney Sparapani [aut, cre] , Robert McCulloch [aut] , Matthew Pratola [ctb] , Hugh Chipman [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Depends on survival, nnet

Suggests knitr, rmarkdown

Linking to Rcpp


See at CRAN