Title: Gaussian Graphical Model selection based on birth-death MCMC
Author: Abdolreza Mohammadi and Ernst Wit
Depends: R (>= 2.14.0), MASS, reshape, network
Description: 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.

Depends: R (>= 2.14.0), reshape, network
main function is 'bdmcmc' which is a brith-death MCMC algorithm
for Bayesian model selection in Gaussian graphical models.

Title: Gaussian Graphical Model determination based on birth-death MCMC
Description: 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

Packaged: 2013-02-05 08:33:47 UTC; Administrator

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
Packaged: 2013-03-17 13:55:16 UTC; Administrator

Title: Graph estimation based on birth-death MCMC approach
Description: This package provides a Bayesian methodology for structure learning in undirected graphical models. Our Bayesian methodology is based on birth-death Markov chain Monte Carlo (BDMCMC) algorithm which is the main function with the name 'bdgraph'. The main target of this package is high-dimensional data analysis wherein usually p >> n. The computation is memory-optimized using the sparse matrix output.
Acknowledgements: The help of the CRAN team to integrate C++ code with

Title: Graph Estimation Based on Birth-Death MCMC Approach

Description: This package provides a general framework to perform Bayesian structure learning in undirected graphical models. The package provides recent improvements in the Bayesian literature. The package consists of two main MCMC sampling algorithm efficiently implemented in C++ to maximize computational speed. The main target of the package is high-dimensional data analysis wherein either continuous or discrete variables and usually number of variables are less than number of observations. Acknowledgements: The help of the CRAN team to integrate C++ code with R is gratefully acknowledged.