Package: nftbart 2.3
Rodney Sparapani
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>.
Authors:
nftbart_2.3.tar.gz
nftbart_2.3.tar.gz(r-4.7-arm64)nftbart_2.3.tar.gz(r-4.7-x86_64)nftbart_2.3.tar.gz(r-4.6-arm64)nftbart_2.3.tar.gz(r-4.6-x86_64)
nftbart_2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
nftbart/json (API)
NEWS
| # Install 'nftbart' in R: |
| install.packages('nftbart', repos = c('https://cran.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 from:2ca4ed21be. Checks:5 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 136 | ||
| source / vignettes | OK | 267 | ||
| linux-release-arm64 | OK | 124 | ||
| linux-release-x86_64 | OK | 135 | ||
| wasm-release | OK | 106 |
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 |
