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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 |
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