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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.3 |
rolling linux/jammy R-4.5 | nftbart_2.3.tar.gz |
361.8 KiB |
2.3 |
rolling linux/noble R-4.5 | nftbart_2.3.tar.gz |
363.7 KiB |
2.3 |
rolling source/ R- | nftbart_2.3.tar.gz |
174.7 KiB |
2.3 |
latest linux/jammy R-4.5 | nftbart_2.3.tar.gz |
361.8 KiB |
2.3 |
latest linux/noble R-4.5 | nftbart_2.3.tar.gz |
363.7 KiB |
2.3 |
latest source/ R- | nftbart_2.3.tar.gz |
174.7 KiB |
2.3 |
2026-04-26 source/ R- | nftbart_2.3.tar.gz |
174.7 KiB |
2.3 |
2026-04-23 source/ R- | nftbart_2.3.tar.gz |
174.7 KiB |
2.3 |
2026-04-09 windows/windows R-4.5 | nftbart_2.3.zip |
767.4 KiB |
2.1 |
2025-04-20 source/ R- | nftbart_2.1.tar.gz |
170.1 KiB |