Getting Started with "ergm": Fit, simulate and diagnose exponential-family models for networks
ergm is a collection of functions to plot, fit, diagnose, and simulate from random
graph models. For a list of functions type: help(package='ergm')
For a complete list of the functions, use
or read the rest of the manual. For a simple demonstration,
When publishing results obtained using this package the original authors
are to be cited as given in
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau,
and Martina Morris. 2003
ergm: Fit, simulate and diagnose exponential-family models for networks
All published work derived from this package must cite it.
For complete citation information, use
Recent advances in the statistical modeling of random networks have had an impact on the empirical study of social networks. Statistical exponential family models (Strauss and Ikeda 1990) are a generalization of the Markov random network models introduced by Frank and Strauss (1986), which in turn derived from developments in spatial statistics (Besag, 1974). These models recognize the complex dependencies within relational data structures. To date, the use of stochastic network models for networks has been limited by three interrelated factors: the complexity of realistic models, the lack of simulation tools for inference and validation, and a poor understanding of the inferential properties of nontrivial models.
This manual introduces software tools for the representation, visualization,
and analysis of network data that address each of these previous shortcomings.
The package relies on the
network package which allows networks to be
represented in R. The
ergm package allows maximum likelihood estimates of
exponential random network models to be calculated using Markov Chain Monte
Carlo. The package also provides tools for plotting networks, simulating
networks and assessing model goodness-of-fit.
For detailed information on how to download and install the software,
go to the
A tutorial, support newsgroup, references and links to further resources are provided there.
Mark S. Handcock email@example.com,
David R. Hunter firstname.lastname@example.org,
Carter T. Butts email@example.com,
Steven M. Goodreau firstname.lastname@example.org,
Pavel N. Krivitsky email@example.com, and
Martina Morris firstname.lastname@example.org
Maintainer: David R. Hunter email@example.com
Admiraal R, Handcock MS (2007). networksis: Simulate bipartite graphs with fixed marginals through sequential importance sampling. Statnet Project, Seattle, WA. Version 1, statnet.org.
Bender-deMoll S, Morris M, Moody J (2008). Prototype Packages for Managing and Animating Longitudinal Network Data: dynamicnetwork and rSoNIA. Journal of Statistical Software, 24(7). http://www.jstatsoft.org/v24/i07/.
Besag, J., 1974, Spatial interaction and the statistical analysis of lattice systems (with discussion), Journal of the Royal Statistical Society, B, 36, 192-236.
Boer P, Huisman M, Snijders T, Zeggelink E (2003). StOCNET: an open software system for the advanced statistical analysis of social networks. Groningen: ProGAMMA / ICS, version 1.4 edition.
Butts CT (2007). sna: Tools for Social Network Analysis. R package version 2.3-2. http://CRAN.R-project.org/package=sna.
Butts CT (2008). network: A Package for Managing Relational Data in R. Journal of Statistical Software, 24(2). http://www.jstatsoft.org/v24/i02/.
Butts C (2015). network: The Statnet Project (http://www.statnet.org). R package version 1.12.0, http://CRAN.R-project.org/package=network.
Frank, O., and Strauss, D.(1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842.
Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A statnet Tutorial. Journal of Statistical Software, 24(8). http://www.jstatsoft.org/v24/i08/.
Goodreau SM, Kitts J, Morris M (2008b). Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks. Demography, 45, in press.
Handcock, M. S. (2003) Assessing Degeneracy in Statistical Models of Social Networks, Working Paper \#39, Center for Statistics and the Social Sciences, University of Washington. www.csss.washington.edu/Papers/wp39.pdf
Handcock MS (2003b). degreenet: Models for Skewed Count Distributions Relevant to Networks. Statnet Project, Seattle, WA. Version 1.0, statnet.org.
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003a). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Statnet Project, Seattle, WA. Version 2, statnet.org.
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003b). statnet: Software Tools for the Statistical Modeling of Network Data. Statnet Project, Seattle, WA. Version 2, statnet.org.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics, 15: 565-583.
Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3). http://www.jstatsoft.org/v24/i03/.
Krivitsky PN, Handcock MS (2007). latentnet: Latent position and cluster models for statistical networks. Seattle, WA. Version 2, statnet.org.
Morris M, Handcock MS, Hunter DR (2008). Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. Journal of Statistical Software, 24(4). http://www.jstatsoft.org/v24/i04/.
Strauss, D., and Ikeda, M.(1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204-212.
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