Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects

Martina Morris, Mark S. Handcock and David R. Hunter

Journal of Statistical Software, 2008, vol. 024, issue i04

Abstract: Exponential-family random graph models (ERGMs) represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo (MCMC) algorithm that the package uses for estimation. These controls affect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.

Date: 2008-05-07
References: View complete reference list from CitEc
Citations: View citations in EconPapers (40)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v024i04/v24i04.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v024i04/v24i04.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:024:i04

DOI: 10.18637/jss.v024.i04

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().