Exponential-family random graph models (ERGMs) represent the processes that gov-
ern 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.
%0 Journal Article
%1 morris_specification_2008
%A Morris, Martina
%A Handcock, Mark S.
%A Hunter, David R.
%D 2008
%J Journal of statistical software
%K Carlo, Markov Monte R, Statistics chain exponential graph model, random
%N 4
%P 1548
%T Specification of exponential-family random graph models: terms and computational aspects
%U http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2481518/
%V 24
%X Exponential-family random graph models (ERGMs) represent the processes that gov-
ern 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.
@article{morris_specification_2008,
abstract = {Exponential-family random graph models (ERGMs) represent the processes that gov-
ern 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.},
added-at = {2017-01-09T13:57:26.000+0100},
author = {Morris, Martina and Handcock, Mark S. and Hunter, David R.},
biburl = {https://www.bibsonomy.org/bibtex/22c45446cb0c0731e3171a3dd2f7832b3/yourwelcome},
interhash = {eb91999b1dfed386d60e08d01d5fc0f0},
intrahash = {2c45446cb0c0731e3171a3dd2f7832b3},
journal = {Journal of statistical software},
keywords = {Carlo, Markov Monte R, Statistics chain exponential graph model, random},
number = 4,
pages = 1548,
shorttitle = {Specification of exponential-family random graph models},
timestamp = {2017-01-09T14:01:11.000+0100},
title = {Specification of exponential-family random graph models: terms and computational aspects},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2481518/},
urldate = {2013-08-11},
volume = 24,
year = 2008
}