Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
hergm: Hierarchical Exponential-Family Random Graph Models | Schweinberger | Journal of Statistical Software
Authors: Michael Schweinberger, Pamela Luna
Title: hergm: Hierarchical Exponential-Family Random Graph Models
Abstract: We describe the R package hergm that implements hierarchical exponential-family random graph models with local dependence. Hierarchical exponential-family random graph models with local dependence tend to be superior to conventional exponential-family random graph models with global dependence in terms of goodness-of-fit. The advantage of hierarchical exponential-family random graph models is rooted in the local dependence induced by them. We discuss the notion of local dependence and the construction of models with local dependence along with model estimation, goodness-of-fit, and simulation. Simulation results and three applications are presented.

Page views:: 618. Submitted: 2016-09-30. Published: 2018-06-09.
Paper: hergm: Hierarchical Exponential-Family Random Graph Models     Download PDF (Downloads: 204)
Supplements:
hergm_3.2-1.tar.gz: R source package Download (Downloads: 21; 135KB)
v85i01.R: R replication code Download (Downloads: 22; 4KB)

DOI: 10.18637/jss.v085.i01

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.