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) + s(x) E where functions f and s 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: |
1.3 |
| Depends: |
R (≥ 3.6), survival, nnet |
| Imports: |
Rcpp |
| LinkingTo: |
Rcpp |
| Suggests: |
knitr, rmarkdown |
| Published: |
2022-03-29 |
| 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 |
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