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 mcw.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	nftbart results

Documentation:

Reference manual:

nftbart.pdf

Downloads:

Package source:	nftbart_2.1.tar.gz
Windows binaries:	r-devel: nftbart_2.1.zip, r-release: nftbart_2.1.zip, r-oldrel: nftbart_2.1.zip
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

Linking:

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