Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportionalhazard assumption, leading to dynamic  i.e. nonconstant 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. Crossvalidation 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.
Package details 


Author  Alireza S. Mahani, Mansour T.A. Sharabiani 
Maintainer  Alireza S. Mahani <alireza.s.mahani@gmail.com> 
License  GPL (>= 2) 
Version  0.9.2 
Package repository  View on CRAN 
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