person("Alan T.", "Arnholt", email = "arnholtat@appstate.edu", role = c("aut", "cre")),
person("Ben", "Evans", email = "ben.evans@temple.edu", role = "aut"))
Maintainer: Alan T. Arnholt <arnholtat@appstate.edu>
Suggests: ggplot2 (>= 2.1.0), dplyr, tidyr

Description: The integration of Bayesian variable and sparse group variable selection approaches for regression models.
Depends: R (>= 3.1.1), MASS, compiler, pscl, plyr
Packaged: 2015-02-11 23:18:39 UTC; Kuojung Lee

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

Title: Bayesian Survival Model with Lasso Shrinkage Using Generalized
Author: Alireza S. Mahani, Mansour T.A. Sharabiani
Maintainer: Alireza S. Mahani <alireza.mahani@sentrana.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.

Maintainer: Alireza S. Mahani <alireza.s.mahani@gmail.com>
Imports: foreach, doParallel, survival, MfUSampler, methods

Title: Bayesian Survival Model using Generalized Weibull Regression
Description: Bayesian survival model using Weibull regression on both scale and shape parameters.

Title: Bayesian Synthetic Likelihood with Graphical Lasso
Authors@R: c(person("Ziwen","An",role=c("aut","cre"),email="ziwen.an@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-9947-5182")),
person("Christopher C.","Drovandi",role=c("ctb"),email="c.drovandi@qut.edu.au",comment = c(ORCID = "0000-0001-9222-8763")),
person("Leah F.","South",role=c("ctb"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
Description: Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>)
is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a
multivariate normal distribution for the summary statistic likelihood and it is suitable when
the distribution of the model summary statistics is sufficiently regular. This package provides
a Metropolis Hastings Markov chain Monte Carlo implementation of BSL and
BSL with graphical lasso (BSLasso, An et al. (2018) <https://eprints.qut.edu.au/102263/>),
which is computationally more efficient when the dimension of the summary statistic is high.
Imports: glasso, ggplot2, stats, MASS, cvTools, grid, gridExtra,
Christopher C. Drovandi [ctb] (<https://orcid.org/0000-0001-9222-8763>),