nftbart: 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>.

Getting started

Package details

AuthorRodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb]
MaintainerRodney Sparapani <rsparapa@mcw.edu>
LicenseGPL (>= 2)
Version1.3
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("nftbart")

Try the nftbart package in your browser

Any scripts or data that you put into this service are public.

nftbart documentation built on March 30, 2022, 1:05 a.m.