CRAN/E | BSGW

BSGW

Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression

Installation

About

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.

Key Metrics

Version 0.9.2
Published 2016-09-21 2561 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks BSGW results

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Maintainer

Maintainer

Alireza S. Mahani

alireza.s.mahani@gmail.com

Authors

Alireza S. Mahani
Mansour T.A. Sharabiani

Material

ChangeLog
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

BSGW archive

Imports

foreach
doParallel
survival
MfUSampler
methods

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