nftbart
2.3Nonparametric Failure Time Bayesian Additive Regression Trees
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 91%
- Return-value docs
- 83%
- References docs
- 67%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
9 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 2.3Latest
- 2.22025-08-24 · diff ↗
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 2.12023-11-28 · diff ↗
- 1.62023-05-01 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.52023-01-06 · diff ↗
- 1.42022-08-26 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 1.32022-03-29 · diff ↗
- 1.22022-02-03 · diff ↗
- 1.12021-12-20
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-12-20
- Total releases
- 9 / 5 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.2.0
- Bundled data
- 59 KB / 3 files
- Download size
- 176 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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