A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
Bayesian Inference for Discrete Weibull Regression
coda, parallel, foreach, doParallel, MASS, methods, graphics,
stats, utils

coda, parallel, foreach, doParallel, MASS, methods, graphics,
stats, utils, DWreg

Gaussian Graphical Model selection based on birth-death MCMC
methodology
BDgraph is a statistical tool for Bayessian model
determination in Gaussian graphical models based on birth-death
MCMC algorithm. This package includes several functions. The
main function is BDMCMC which is brith-death MCMC algorithm for
Bayesian model selection in Gaussian graphical models.

BDgraph is a statistical tool for Bayessian model
determination in Gaussian graphical models based on birth-death
MCMC algorithm. This package includes several functions. The
main function is 'bdmcmc' which is a brith-death MCMC algorithm
for Bayesian model selection in Gaussian graphical models.

Gaussian Graphical Model determination based on birth-death MCMC
methodology
The R package BDgraph is a statistical tool for Bayesian
model determination in undirected Gaussian graphical models.
The Bayesian methodology is based on birth-death MCMC
algorithm. The main function is 'bdmcmc' which is a birth-death
MCMC algorithm for Bayesian model determination in undirected
Gaussian graphical models.

The R package BDgraph is a statistical tool for Bayesian
model selection in undirected Gaussian graphical models. Our
Bayesian methodology is based on birth-death MCMC algorithm.
The main function is 'bdmcmc' which is a birth-death MCMC
algorithm for Bayesian model selection in undirected Gaussian
graphical models.