Bayesian Exponential Random Graph Models

Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <>.

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.


How to cite Bergm

Alberto Caimo, Nial Friel (2014). Bergm: Bayesian Exponential Random Graphs in R. Journal of Statistical Software, 61(2), 1-25. URL


Reference manual

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5.0.1 by Alberto Caimo, 7 months ago

Browse source code at

Authors: Alberto Caimo [aut, cre] , Lampros Bouranis [aut] , Robert Krause [aut] Nial Friel [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports network, coda, MCMCpack, Matrix, mvtnorm, matrixcalc

Depends on ergm

See at CRAN