Bergm: Bayesian Exponential Random Graphs in R
|Title:||Bergm: Bayesian Exponential Random Graphs in R||Authors:||Caimo, Alberto
|Permanent link:||http://hdl.handle.net/10197/8358||Date:||24-Oct-2014||Abstract:||In this paper we describe the main features of the Bergm package for the open-source Rsoftware which provides a comprehensive framework for Bayesian analysis of exponentialrandom graph models: tools for parameter estimation, model selection and goodness-of-t diagnostics. We illustrate the capabilities of this package describing the algorithmsthrough a tutorial analysis of three network datasets.||Type of material:||Journal Article||Publisher:||Foundation for Open Access Statistics||Copyright (published version):||2014 the Authors||Keywords:||Machine learning;Statistics;Exponential random graph models;Bayesian inference;Bayesian model selection;Markov chain Monte Carlo||DOI:||10.18637/jss.v061.i02||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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