Package: BSGW
Type: Package
Title: Bayesian Survival Model with Lasso Shrinkage Using Generalized
        Weibull Regression
Version: 0.9.4
Date: 2022-12-12
Author: Alireza S. Mahani, Mansour T.A. Sharabiani
Maintainer: Alireza S. Mahani <alireza.s.mahani@gmail.com>
Description: 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.
License: GPL (>= 2)
Imports: foreach, doParallel, survival, MfUSampler, methods
NeedsCompilation: no
Repository: CRAN
Packaged: 2022-12-12 11:55:08 UTC; ec2-user
Date/Publication: 2022-12-12 13:10:08 UTC
Built: R 4.2.0; ; 2023-07-11 00:15:18 UTC; unix
