Statistical Network Analysis with Bergm
Dublin Core
Title
Statistical Network Analysis with Bergm
Subject
Bayesian inference, exponential random graph models, R packages.
Description
Recent advances in computational methods for intractable models have made network
data increasingly amenable to statistical analysis. Exponential random graph models
(ERGMs) emerged as one of the main families of models capable of capturing the complex
dependence structure of network data in a wide range of applied contexts. The Bergm
package for R has become a popular package to carry out Bayesian parameter inference,
missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs.
Over the last few years, the package has been considerably improved in terms of efficiency
by adopting some of the state-of-the-art Bayesian computational methods for doublyintractable distributions. Recently, version 5 of the package has been made available on
CRAN having undergone a substantial makeover, which has made it more accessible and
easy to use for practitioners. New functions include data augmentation procedures based
on the approximate exchange algorithm for dealing with missing data, adjusted pseudolikelihood and pseudo-posterior procedures, which allow for fast approximate inference of
the ERGM parameter posterior and model evidence for networks on several thousands
nodes
data increasingly amenable to statistical analysis. Exponential random graph models
(ERGMs) emerged as one of the main families of models capable of capturing the complex
dependence structure of network data in a wide range of applied contexts. The Bergm
package for R has become a popular package to carry out Bayesian parameter inference,
missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs.
Over the last few years, the package has been considerably improved in terms of efficiency
by adopting some of the state-of-the-art Bayesian computational methods for doublyintractable distributions. Recently, version 5 of the package has been made available on
CRAN having undergone a substantial makeover, which has made it more accessible and
easy to use for practitioners. New functions include data augmentation procedures based
on the approximate exchange algorithm for dealing with missing data, adjusted pseudolikelihood and pseudo-posterior procedures, which allow for fast approximate inference of
the ERGM parameter posterior and model evidence for networks on several thousands
nodes
Creator
Alberto Caimo
Source
https://www.jstatsoft.org/article/view/v104i01
Publisher
TU Dublin
Date
September 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Alberto Caimo, “Statistical Network Analysis with Bergm,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8271.