Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) and Mohammadi et al. (2017) .


News

  • CHANGES IN VERSION 2.19 The Title in Description is changed Function "I.g" is added

  • CHANGES IN VERSION 2.20 Reversible jump MCMC algorithm is added to "bdgraph()" fonction

  • CHANGES IN VERSION 2.23 Function "I.g" is chenged to "log_Ig" and it is implemented in C++

  • CHANGES IN VERSION 2.24 Function "phat" is changed to "plinks" Function "prob" is changed to "pgraph" Function "log_Ig" is changed to "gnorm"

  • CHANGES IN VERSION 2.28 The Title in Description is changed Function "bdgraph.ts", "rgcwish", and "rcwish" are added to the package

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("BDgraph")

2.36 by Abdolreza Mohammadi, a month ago


https://www.tilburguniversity.edu/webwijs/show/a.mohammadi.htm


Browse source code at https://github.com/cran/BDgraph


Authors: Abdolreza Mohammadi and Ernst Wit


Documentation:   PDF Manual  


Task views: gRaphical Models in R


GPL (>= 2) license


Depends on Matrix, igraph


Depended on by bmixture.


See at CRAN