knitr, rmarkdown, testthat, sf, surveillance, spdep,
RColorBrewer, deldir, RSQLite

c(
person(given = "Rowland",
family = "Seymour",
role = c("aut", "cre"),
email = "rowland.seymour@nottingham.ac.uk",
comment = c(ORCID = "0000-0002-8739-3921")),
person(given = "James",
family = "Briant",
role = c("aut"),
email = "james@briant.co.uk"),
person(given = "Yichi",
family = "Zhang",
role = c("aut"),
email = "smyyz6@nottingham.ac.uk")
)
Rowland Seymour [aut, cre] (<https://orcid.org/0000-0002-8739-3921>),
James Briant [aut],
Yichi Zhang [aut]

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens

Data sets for book "Basic Statistics and Data Analysis" by
Larry J. Kitchens

c(
person("Alan T.", "Arnholt", email = "arnholtat@appstate.edu", role = c("aut", "cre")),
person("Ben", "Evans", email = "ben.evans@temple.edu", role = "aut"))
Data sets for book "Basic Statistics and Data Analysis" by
Larry J. Kitchens.
Alan T. Arnholt [aut, cre],
Ben Evans [aut]

The integration of Bayesian variable and sparse group variable selection approaches for regression models.
R (>= 3.1.1), MASS, compiler, pscl, plyr

R (>= 3.1.1), MASS, compiler, pscl, plyr, batchmeans

Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Alireza S. Mahani, Mansour T.A. Sharabiani
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.

foreach, doParallel, survival, MfUSampler, methods