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 |

**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)...

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
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.