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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:

Please use the canonical form https://CRAN.R-project.org/package=nftbart to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.