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 description of the model at <doi:10.1111/biom.13857>.
| Version: |
2.2 |
| Depends: |
R (≥ 4.2.0), survival, nnet, lattice |
| Imports: |
Rcpp |
| LinkingTo: |
Rcpp |
| Published: |
2025-08-23 |
| 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 [issues need fixing before 2026-01-15] |
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