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) + 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>.

Version: 2.1
Depends: R (≥ 4.2.0), survival, nnet
Imports: Rcpp
LinkingTo: Rcpp
Published: 2023-11-28
DOI: 10.32614/CRAN.package.nftbart
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: README NEWS
CRAN checks: nftbart results


Reference manual: nftbart.pdf


Package source: nftbart_2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): nftbart_2.1.tgz, r-oldrel (arm64): nftbart_2.1.tgz, r-release (x86_64): nftbart_2.1.tgz, r-oldrel (x86_64): nftbart_2.1.tgz
Old sources: nftbart archive


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