Bayesian Exponential Random Graph Models

Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: < https://acaimo.github.io/Bergm/>.


Bergm: Bayesian Exponential Random Graph Models

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.

Website: https://acaimo.github.io/Bergm


How to cite Bergm

Caimo, A., Bouranis, L., Krause, R., and Friel, N. (2014). Statistical Network Analysis with Bergm. Journal of Statistical Software, 104(1), 1–23. doi: https://doi.org/10.18637/jss.v104.i01.

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("Bergm")

5.0.7 by Alberto Caimo, 3 years ago


https://acaimo.github.io/Bergm/


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


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports coda, graphics, grDevices, Matrix, matrixcalc, MCMCpack, mvtnorm, network, Rglpk, statnet.common, stats, utils

Depends on ergm

Suggests spelling


Imported by BFpack.

Suggested by btergm.

Enhanced by texreg.


See at CRAN