statnet-package {statnet} | R Documentation |
statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling: tools for model estimation, for model evaluation, for model-based network simulation, and for network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.
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 other detailed information on how to download and install the software,
go to the ergm
website:
http://statnet.org.
A tutorial, support newsgroup, references and links to further resources are provided there.
statnet is written in a combination of
R and (ANSI standard) C
It is usually used interactively
from within the R graphical user interface
via a command line. it can also be used in non-interactive (or “batch”) mode
to allow longer or multiple tasks to be processed without user interaction.
The suite of packages are available on the Comprehensive
R Archive Network (CRAN) at http://www.r-project.org/ and also
on the statnet project website at http://statnet.org/
The statnet suite of packages includes two required interdependent components and several optional components that provide additional functionality. Currently, there are four optional components available on CRAN, and another that is available from the author.
Required component packages: ergm and network
ergm is a collection of functions to fit, simulate from, plot and
evaluate exponential random graph models. The main functions within the
ergm package are ergm
, a function to fit linear exponential random
graph models in which the probability of a graph is dependent upon a vector of
graph statistics specified by the user; simulate
, a function to simulate
random graphs using an ERGM; and gof
, a function to evaluate the goodness
of fit of an ERGM to the data. ergm contains many other functions as
well.
network is a package to create, store, modify and plot the data in
network objects.
The network
object
class, defined in the network package,
can represent a range of relational data types and it supports arbitrary
vertex / edge /graph attributes. Data stored as network
objects
can then be analyzed using all of the component packages
in the statnet suite.
Optional components, available on CRAN: sna, degreenet, latentnet, netperm, degreenet and networksis
sna: A set of tools for traditional social network analysis.
degreenet: A package for the statistical modeling of degree distributions of networks. It includes power-law models such as the Yule and Waring, as well as a range of alternative models that have been proposed in the literature.
latentnet: A package to fit and evaluate latent position and cluster models for statistical networks The probability of a tie is expressed as a function of distances between these nodes in a latent space as well as functions of observed dyadic level covariates.
netperm: A package for permutation Models for relational data. It provides simulation and inference tools for exponential families of permutation models on relational structures.
degreenet: A package to fit, simulate and diagnose models for skewed count distributions relevant to networks. It was developed for the degree distributions of networks. It implements likelihood-based inference, bootstrapping, model selection, etc.
networksis: A package to simulate bipartite graphs with fixed marginals through sequential importance sampling
Available on request: dynamicnetwork and rSonia
dynamicnetwork: A set of tools for visualizing dynamically changing networks.
rSonia: provides a set of methods to facilitate exporting data and parameter settings and launching SoNIA (Social Network Image Animator). SoNIA facilitates interactive browsing of dynamic network data and exporting animations as a QuickTime movies.
The entire statnet can be installed and/or updated
while in R using the update.statnet
command.
This gives the users options to install the component packages.
Each of these components is described in detail in the references below.
Loading this base statnet package into R
automatically loads the network and ergm packages.
The optional
packages can be loaded
while in statnet using the library
command.
Each package has associated help files and internal documentation that is
supported by the information on the website (http://statnet.org/).
When publishing results obtained using this package the original authors are to be cited as:
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau,
and Martina Morris. 2003
statnet: Software tools for the Statistical Modeling of Network Data
http://statnet.org.
We have invested a lot of time and effort in creating the
statnet
suite of packages for use by other researchers.
lease cite it in all papers where it is used.
For complete citation information, use
citation(package="statnet")
.
Mark S. Handcock handcock@stat.washington.edu,
David R. Hunter dhunter@stat.psu.edu,
Carter T. Butts buttsc@uci.edu,
Steven M. Goodreau goodreau@u.washington.edu,
Pavel N. Krivitsky pavel@cmu.edu, and
Martina Morris morrism@u.washington.edu
Maintainer: Mark S. Handcock handcock@stat.washington.edu
Admiraal R, Handcock MS (2007). networksis: Simulate bipartite graphs with fixed marginals through sequential importance sampling. Statnet Project, Seattle, WA. Version 1, http://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.
Butts CT (2006). netperm: Permutation Models for Relational Data. Version 0.2, http://erzuli.ss.uci.edu/R.stuff.
Butts CT (2007). sna: Tools for Social Network Analysis. Version 1.5, http://erzuli.ss.uci.edu/R.stuff.
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 CT, with help~from David~Hunter, Handcock MS (2007). network: Classes for Relational Data. Version 1.3, http://erzuli.ss.uci.edu/R.stuff.
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. Project homepage at http://statnet.org, URL: http://CRAN.R-project.org/package=degreenet.
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. Project homepage at http://statnet.org, URL: http://CRAN.R-project.org/package=ergm.
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. Project homepage at http://statnet.org, URL: http://CRAN.R-project.org/package=statnet.
Hunter, D. R. and Handcock, M. S. (2006) Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics.
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 (2008). Fitting Latent Cluster Models for Social Networks with latentnet. Journal of Statistical Software, 24(5). http://www.jstatsoft.org/v24/i05/.
Krivitsky PN, Handcock MS (2007). latentnet: Latent position and cluster models for statistical networks. Seattle, WA. Version 2. Project homepage at http://statnet.org, URL: http://CRAN.R-project.org/package=latentnet.
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.