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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 |
| 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 |
| Reference manual: | nftbart.pdf |
| 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 |
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