Bergm: Bayesian exponential random graphs in R

Alberto Caimo; Nial Friel

doi:10.18637/jss.v061.i02

Bergm: Bayesian exponential random graphs in R

Alberto Caimo
, Nial Friel

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper we describe the main features of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis of exponential random graph models: tools for parameter estimation, model selection and goodness-of- fit diagnostics. We illustrate the capabilities of this package describing the algorithms through a tutorial analysis of three network datasets.

Original language	English
Journal	Journal of Statistical Software
Volume	61
Issue number	2
DOIs	https://doi.org/10.18637/jss.v061.i02
Publication status	Published - 1 Oct 2014
Externally published	Yes

Keywords

Bayesian inference
Bayesian model selection
Exponential random graph models
Markov chain Monte Carlo

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10.18637/jss.v061.i02

https://arrow.tudublin.ie/scschmatart/212Licence: CC BY-NC-SA

Cite this

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title = "Bergm: Bayesian exponential random graphs in R",

abstract = "In this paper we describe the main features of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis of exponential random graph models: tools for parameter estimation, model selection and goodness-of- fit diagnostics. We illustrate the capabilities of this package describing the algorithms through a tutorial analysis of three network datasets.",

keywords = "Bayesian inference, Bayesian model selection, Exponential random graph models, Markov chain Monte Carlo",

author = "Alberto Caimo and Nial Friel",

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AU - Friel, Nial

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AB - In this paper we describe the main features of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis of exponential random graph models: tools for parameter estimation, model selection and goodness-of- fit diagnostics. We illustrate the capabilities of this package describing the algorithms through a tutorial analysis of three network datasets.

KW - Bayesian inference

KW - Bayesian model selection

KW - Exponential random graph models

KW - Markov chain Monte Carlo

UR - https://www.scopus.com/pages/publications/84908241642

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DO - 10.18637/jss.v061.i02

M3 - Article

SN - 1548-7660

VL - 61

JO - Journal of Statistical Software

JF - Journal of Statistical Software

IS - 2

ER -

Bergm: Bayesian exponential random graphs in R

Abstract

Keywords

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