tergm is a collection of extensions to
the ergm package to fit, diagnose, and
simulate models for dynamic networks --- networks that evolve over
time --- based on exponential-family random graph models (ERGMs). For
a list of functions type help(package='tergm')
When publishing results obtained using this package, please cite the
original authors as described in citation(package="tergm").
All programs derived from this package must cite it.
An exponential-family random graph model (ERGM) postulates an
exponential family over the sample space of networks of interest, and
ergm package implements a suite of tools
for modeling single networks using ERGMs.
More recently, there has been a number of extensions of ERGMs to model evolution of networks, including the temporal ERGM (TERGM) of Hanneke et al. (2010) and the separable termporal ERGM (STERGM) of Krivitsky and Handcock (2013). The latter model allows familiar ERGM terms and statistics to be reused in a dynamic context, interpreted in terms of formation and dissolution of ties. Krivitsky (2012) suggested a method for fitting dyanmic models when only a cross-sectional network is available, provided some temporal information for it is available as well.
This package aims to implement these and other ERGM-based models for
network evoluation. At this time, it implements, via the
stergm function, the STERGMs, both a conditional MLE
(CMLE) fitting to a series of networks and an Equilibrium Generalized
Method of Moments Estimation (EGMME) for fitting to a single network
with temporal information. For further development, see the referenced
papers.
For detailed information on how to download and install the software, go to the Statnet project website: statnet.org. A tutorial, support newsgroup, references and links to further resources are provided there.
Hanneke S, Fu W, and Xing EP (2010). Discrete Temporal Models
of Social Networks. Electronic Journal of Statistics, 2010, 4,
585-605. doi:10.1214/09-EJS548
Krivitsky PN, Handcock MS (2013). A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, In Press. http://arxiv.org/abs/1011.1937
Krivitsky, P.N. (2012). Modeling of Dynamic Networks based on Egocentric Data with Durational Information. Pennsylvania State University Department of Statistics Technical Report, 2012(2012-01). http://stat.psu.edu/research/technical-report-files/2012-technical-reports/modeling-of-dynamic-networks-based-on-egocentric-data-with-durational-information
Butts CT (2008). network: A Package for Managing Relational Data in R. Journal of Statistical Software, 24(2). http://www.jstatsoft.org/v24/i02/.
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/.
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Krivitsky P, and Morris M (2012). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Statnet Project, Seattle, WA. Version 3, statnet.org.
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Krivitsky P, Morris M (2012). statnet: Software Tools for the Statistical Modeling of Network Data. Statnet Project, Seattle, WA. Version 3, 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/.
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/.