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 technical description of the model <https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr72.pdf?la=en>.

Version: 1.4
Depends: R (≥ 3.6), survival, nnet
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2022-08-25
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: NEWS
CRAN checks: nftbart results

Documentation:

Reference manual: nftbart.pdf

Downloads:

Package source: nftbart_1.4.tar.gz
Windows binaries: r-devel: nftbart_1.4.zip, r-release: nftbart_1.4.zip, r-oldrel: nftbart_1.4.zip
macOS binaries: r-release (arm64): nftbart_1.4.tgz, r-oldrel (arm64): nftbart_1.4.tgz, r-release (x86_64): nftbart_1.4.tgz, r-oldrel (x86_64): nftbart_1.4.tgz
Old sources: nftbart archive

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