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README.md
In Bergm: Bayesian Exponential Random Graph Models
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: http://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.
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Bergm documentation built on Sept. 13, 2023, 9:06 a.m.
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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: http://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.
Try the Bergm package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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