| tergm-package {tergm} | R Documentation |
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.
There have been a number of extensions of ERGMs for modeling the evolution of networks, including the temporal ERGM (TERGM) of Hanneke et al. (2010) and the separable termporal ERGM (STERGM) of Krivitsky and Handcock (2014). The latter model allows familiar ERGM terms and statistics to be reused in a dynamic context, interpreted in terms of formation and dissolution (persistence) of ties. Krivitsky (2012) suggested a method for fitting dynamic 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 tergm
function, a general framework for modeling tie dynamics in temporal networks
with flexible model specification (including (S)TERGMs). Estimation options
include a conditional MLE (CMLE) approach for 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.
If you previously used the stergm() function in this package, please
note that stergm() has been superceded by the new tergm
function, and is likely to be deprecated in future releases. The
dissolution formula in stergm() maps to the new Persist()
operator in the tergm() function, not the Diss() operator.
For detailed information on how to download and install the software, go to the Statnet project website: https://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
Krackhardt, D and Handcock, MS (2006) Heider vs Simmel: Emergent features in dynamic structures. ICML Workshop on Statistical Network Analysis. Springer, Berlin, Heidelberg, 2006.
Krivitsky PN & Handcock MS (2014) A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society, Series B, 76(1): 29-46. doi: 10.1111/rssb.12014
Krivitsky, PN (2012). Modeling of Dynamic Networks based on Egocentric Data with Durational Information. Pennsylvania State University Department of Statistics Technical Report, 2012(2012-01). https://web.archive.org/web/20170830053722/https://stat.psu.edu/research/technical-report-files/2012-technical-reports/TR1201A.pdf
Butts CT (2008). network: A Package for Managing Relational Data in . Journal of Statistical Software, 24(2). https://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). https://www.jstatsoft.org/v24/i08/.
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). https://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). https://www.jstatsoft.org/v24/i04/.