Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: < https://acaimo.github.io/Bergm>.
Bergm provides a comprehensive framework for Bayesian parameter estimation and model selection for exponential random graph models using advanged computational algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy and missing data imputation.
Alberto Caimo, Nial Friel (2014). Bergm: Bayesian Exponential Random Graphs in R. Journal of Statistical Software, 61(2), 1-25. URL http://www.jstatsoft.org/v61/i02/.