Graph estimation based on birth-death MCMC approach
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.
The help of the CRAN team to integrate C++ code with
R is gratefully acknowledged.

Graph Estimation Based on Birth-Death MCMC Approach

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.

A general framework to perform Bayesian structure learning in undirected graphical models. The main target is high-dimensional data analysis wherein either continuous or discrete variables.

Bayesian Graph Selection Based on Birth-Death MCMC Approach

Bayesian undirected graph estimation with BD-MCMC

Bayesian inference for structure learning in undirected graphical models. The main target is to uncover complicated patterns in multivariate data wherein either continuous or discrete variables.

Provides statistical tools for Bayesian structure learning in undirected graphical models with either continuous or discrete variables. The main target is to uncover complicated patterns in multivariate data wherein either continuous or discrete variables.

Provides statistical tools for Bayesian structure learning in undirected graphical models with either continuous or discrete variables.