Bergm: Bayesian Exponential Random Graphs in R

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Title: Bergm: Bayesian Exponential Random Graphs in R
Authors: Caimo, Alberto
Friel, Nial
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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 learningStatisticsExponential random graph modelsBayesian inferenceBayesian model selectionMarkov chain Monte Carlo
DOI: 10.18637/jss.v061.i02
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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