tergm-package: tergm: Fit, Simulate and Diagnose Models for Network...
In tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
tergm-package R Documentation
tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
Description
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12014")} and Carnegie, Krivitsky, Hunter, and Goodreau (2015) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2014.903087")}.
Details
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
evolution. 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.
Temporal model specification in tergm
The operator terms implemented by tergm are Form(),
Persist(), Diss(), Cross(), and Change(). These are used
to specify how the ergm terms (ergmTerm) in a formula are
evaluated across a network time-series. Note, you cannot use one
of these operators within another temporal, so
Cross(~Form(~edges)) is not a valid specification. (Generally,
nesting these operators within other operators will often not work;
nesting other operators within them will almost always work,
however.)
The durational terms are distinguished either by their name,
mean.age, or their name extensions: <name>.ages,
<name>.mean.age, and <name>.age.interval. In contrast to
their eponymous terms in ergm, these durational
terms take into account the elapsed time since each (term-relevant) dyad in
the network was last toggled.
As currently implemented, the package does not support use of many
durational terms during estimation, though it may work with some.
But durational terms may be used as targets, monitors, or summary
statistics. The ability to use these terms in the estimation of
models is under development.
Compatibility with previous versions
If you previously used the stergm() function in this package, please
note that stergm() has been superceded by the new tergm()
function, and has been deprecated. 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.
Author(s)
Maintainer: Pavel N. Krivitsky pavel@statnet.org (ORCID)
Authors:
Mark S. Handcock handcock@stat.ucla.edu [thesis advisor]
Other contributors:
David R. Hunter dhunter@stat.psu.edu [contributor]
Steven M. Goodreau goodreau@u.washington.edu [contributor, thesis advisor]
Martina Morris morrism@u.washington.edu [contributor, thesis advisor]
Nicole Bohme Carnegie nicole.carnegie@nyu.edu [contributor]
Carter T. Butts buttsc@uci.edu [contributor]
Ayn Leslie-Cook aynlc3@uw.edu [contributor]
Skye Bender-deMoll skyebend@skyeome.net [contributor]
Li Wang lxwang@gmail.com [contributor]
Kirk Li kirkli@uw.edu [contributor]
Chad Klumb cklumb@gmail.com [contributor]
Adrien Le Guillou git@aleguillou.org (ORCID) [contributor]
References
Hanneke S, Fu W and Xing EP (2010). Discrete
Temporal Models of Social Networks. Electronic Journal of Statistics,
2010, 4, 585-605.
\Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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).
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i02")}
Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A
statnet Tutorial. Journal of Statistical Software, 24(8).
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.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).
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.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).
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i04")}
See Also
Useful links:
-
Report bugs at https://github.com/statnet/tergm/issues
tergm documentation built on Oct. 8, 2024, 5:09 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| tergm-package | R Documentation |
tergm: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
Description
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12014")} and Carnegie, Krivitsky, Hunter, and Goodreau (2015) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2014.903087")}.
Details
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
evolution. 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.
Temporal model specification in tergm
The operator terms implemented by tergm are Form(),
Persist(), Diss(), Cross(), and Change(). These are used
to specify how the ergm terms (ergmTerm) in a formula are
evaluated across a network time-series. Note, you cannot use one
of these operators within another temporal, so
Cross(~Form(~edges)) is not a valid specification. (Generally,
nesting these operators within other operators will often not work;
nesting other operators within them will almost always work,
however.)
The durational terms are distinguished either by their name,
mean.age, or their name extensions: <name>.ages,
<name>.mean.age, and <name>.age.interval. In contrast to
their eponymous terms in ergm, these durational
terms take into account the elapsed time since each (term-relevant) dyad in
the network was last toggled.
As currently implemented, the package does not support use of many durational terms during estimation, though it may work with some. But durational terms may be used as targets, monitors, or summary statistics. The ability to use these terms in the estimation of models is under development.
Compatibility with previous versions
If you previously used the stergm() function in this package, please
note that stergm() has been superceded by the new tergm()
function, and has been deprecated. 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.
Author(s)
Maintainer: Pavel N. Krivitsky pavel@statnet.org (ORCID)
Authors:
Mark S. Handcock handcock@stat.ucla.edu [thesis advisor]
Other contributors:
David R. Hunter dhunter@stat.psu.edu [contributor]
Steven M. Goodreau goodreau@u.washington.edu [contributor, thesis advisor]
Martina Morris morrism@u.washington.edu [contributor, thesis advisor]
Nicole Bohme Carnegie nicole.carnegie@nyu.edu [contributor]
Carter T. Butts buttsc@uci.edu [contributor]
Ayn Leslie-Cook aynlc3@uw.edu [contributor]
Skye Bender-deMoll skyebend@skyeome.net [contributor]
Li Wang lxwang@gmail.com [contributor]
Kirk Li kirkli@uw.edu [contributor]
Chad Klumb cklumb@gmail.com [contributor]
Adrien Le Guillou git@aleguillou.org (ORCID) [contributor]
References
Hanneke S, Fu W and Xing EP (2010). Discrete Temporal Models of Social Networks. Electronic Journal of Statistics, 2010, 4, 585-605. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i02")}
Goodreau SM, Handcock MS, Hunter DR, Butts CT, Morris M (2008a). A statnet Tutorial. Journal of Statistical Software, 24(8). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v024.i04")}
See Also
Useful links:
Report bugs at https://github.com/statnet/tergm/issues
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.