Package: nftbart 2.1
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 complete description of the model at <doi:10.1111/biom.13857>.
Authors:
nftbart_2.1.tar.gz
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nftbart.pdf |nftbart.html✨
nftbart/json (API)
NEWS
| # Install 'nftbart' in R: |
| install.packages('nftbart', repos = c('https://rsparapa.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:9327361c87. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 09 2024 |
| R-4.5-win-x86_64 | NOTE | Sep 09 2024 |
| R-4.5-linux-x86_64 | NOTE | Sep 09 2024 |
| R-4.4-win-x86_64 | NOTE | Sep 09 2024 |
| R-4.4-mac-x86_64 | NOTE | Sep 09 2024 |
| R-4.4-mac-aarch64 | NOTE | Sep 09 2024 |
| R-4.3-win-x86_64 | NOTE | Sep 09 2024 |
| R-4.3-mac-x86_64 | NOTE | Sep 09 2024 |
| R-4.3-mac-aarch64 | NOTE | Sep 09 2024 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Deprecated: use bMM instead | bartModelMatrix |
| Create a matrix out of a vector or data.frame | bMM |
| NHANES 1999-2000 Body Measures and Demographics | bmx |
| CDC height for age growth charts | CDCheight |
| Cold-deck missing imputation | CDimpute |
| Calculate the C-index/concordance for survival analysis. | Cindex concordance |
| NCCTG Lung Cancer Data | cancer lung |
| Fit NFT BART models. | nft nft2 |
| Estimating the survival and the hazard for AFT BART models. | predict.aftree |
| Drawing Posterior Predictive Realizations for NFT BART models. | predict.nft predict.nft2 |
| Variable selection with NFT BART models. | tsvs tsvs2 |
| Specifying cut-points for the covariates | xicuts |
