| 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), as well as latent space models and more traditional network methods. 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.
statnet packages are written in a combination of R and
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 has the following components:
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.
tergm is a collection of extentions to ergm enabling it to fit models for dynamic networks.
ergm.count is an extension to ergm enabling it to fit models for networks whose relations are counts.
ergm.userterms provides a template for implementing new ERGM terms.
sna is a set of tools for traditional social network analysis.
degreenet is 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 is 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.
networksis is a package to simulate bipartite graphs with fixed marginals through sequential importance sampling.
relevent is a package providing tools to fit relational event models.
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.
networkDynamic extends network with functionality
to store information about about evolution of a network over time,
defining a networkDynamic object
class.
In addition, the following packages are available from the author:
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.
statnet is a metapackage, depending on all of the above packages, so that they can be installed together.
Each of these components is described in detail in the references below. Loading the statnet package into R automatically loads them all. Each package has associated help files and internal documentation that is supported by the information on the Statnet Project website (http://statnet.org/). A tutorial, support mailing list, references and links to further resources are provided there.
When publishing results obtained using this package the original
authors are to be cited as described in
citation("statnet"). In addition, please cite the specific
package that you use.
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.
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: Pavel N. Krivitsky krivitsky@stat.psu.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 (2012). Exponential-Family Random Graph Models for Valued
Networks. Electronic Journal of Statistics, 2012, 6,
1100-1128. doi:10.1214/12-EJS696
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.