The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
| Version: | 0.9.4 |
| Imports: | foreach, doParallel, survival, MfUSampler, methods |
| Published: | 2022-12-12 |
| DOI: | 10.32614/CRAN.package.BSGW |
| Author: | Alireza S. Mahani, Mansour T.A. Sharabiani |
| Maintainer: | Alireza S. Mahani <alireza.s.mahani at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | BSGW results |
| Reference manual: | BSGW.pdf |
| Package source: | BSGW_0.9.4.tar.gz |
| Windows binaries: | r-devel: BSGW_0.9.4.zip, r-release: BSGW_0.9.4.zip, r-oldrel: BSGW_0.9.4.zip |
| macOS binaries: | r-release (arm64): BSGW_0.9.4.tgz, r-oldrel (arm64): BSGW_0.9.4.tgz, r-release (x86_64): BSGW_0.9.4.tgz, r-oldrel (x86_64): BSGW_0.9.4.tgz |
| Old sources: | BSGW archive |
Please use the canonical form https://CRAN.R-project.org/package=BSGW to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.