BSGW: Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression

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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.

Author
Alireza S. Mahani, Mansour T.A. Sharabiani
Date of publication
2016-09-21 08:06:29
Maintainer
Alireza S. Mahani <alireza.s.mahani@gmail.com>
License
GPL (>= 2)
Version
0.9.2

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Man pages

bsgw
Bayesian Survival using Generalized Weibull Regression
crossval_bsgw
Convenience functions for cross-validation-based selection of...
plot_bsgw
Plot diagnostics for a bsgw object
predict_bsgw
Predict method for bsgw model fits
summary_bsgw
Summarizing Bayesian Survival Generalized Weibull (BSGW)...

Files in this package

BSGW
BSGW/NAMESPACE
BSGW/R
BSGW/R/utils.R
BSGW/R/Sample.R
BSGW/R/BSGW.R
BSGW/R/zzz.R
BSGW/MD5
BSGW/DESCRIPTION
BSGW/ChangeLog
BSGW/man
BSGW/man/predict_bsgw.Rd
BSGW/man/plot_bsgw.Rd
BSGW/man/bsgw.Rd
BSGW/man/crossval_bsgw.Rd
BSGW/man/summary_bsgw.Rd