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 <>.

Version: 1.3
Depends: R (≥ 3.6), survival, nnet
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2022-03-29
Author: Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb]
Maintainer: Rodney Sparapani <rsparapa at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: nftbart results


Reference manual: nftbart.pdf


Package source: nftbart_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): nftbart_1.3.tgz, r-oldrel (arm64): nftbart_1.3.tgz, r-release (x86_64): nftbart_1.3.tgz, r-oldrel (x86_64): nftbart_1.3.tgz
Old sources: nftbart archive


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