Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression R package BSGW version 0.9.4 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. Software Imports: foreach, doParallel, survival, MfUSampler, methods Alireza S. Mahani <alireza.s.mahani@gmail.com> Comprehensive R Archive Network (CRAN) Alireza S. Mahani, Mansour T.A. Sharabiani GPL (>= 2) 2022-12-12 application/tgz https://CRAN.R-project.org/package=BSGW doi:10.32614/CRAN.package.BSGW