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Bergm: Bayesian Exponential Random Graphs in R

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Download insight_publication.pdf1.75 MB
Author(s)
Caimo, Alberto 
Friel, Nial 
Uri
http://hdl.handle.net/10197/8358
Date Issued
24 October 2014
Date Available
17T14:56:22Z February 2017
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
Journal
Journal of Statistical Software
Volume
61
Issue
2
Start Page
1
End Page
25
Copyright (Published Version)
2014 the Authors
Keywords
  • Machine learning

  • Statistics

  • Exponential random gr...

  • Bayesian inference

  • Bayesian model select...

  • Markov chain Monte Ca...

DOI
10.18637/jss.v061.i02
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Scopus© citations
35
Acquisition Date
Jan 26, 2023
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