Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals | Zendy

Philip Leifeld | Zendy; Skyler Cranmer | Zendy; Bruce Desmarais | Zendy

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Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals

Author(s) -

Philip Leifeld,

Skyler Cranmer,

Bruce Desmarais

Publication year - 2018

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v083.i06

Subject(s) - exponential random graph models , graph , exponential function , random effects model , computer science , random graph , r package , exponential family , statistics , confidence interval , mathematics , theoretical computer science , meta analysis , medicine , mathematical analysis

The xergm package is an implementation of extensions to the exponential random graph model (ERGM). It acts as a meta-package for multiple constituent packages. One of these packages is btergm, which implements bootstrap methods for the temporal ERGM estimated by maximum pseudolikelihood. Here, we illustrate the temporal exponential random graph model and its implementation in the package btergm using data on international alliances and a longitudinally observed friendship network in a Dutch school.

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