statnet-package: A Suite of Packages for the Statistical Modeling of Network Data

Description

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.

Arguments

Details

statnet packages are written in a combination of Rand C It is usually used interactively from within the Rgraphical 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 RArchive 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:

ergmis a collection of functions to fit, simulate from, plot and evaluate exponential random graph models. The main functions within theergmpackage areergm, 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; andgof, a function to evaluate the goodness of fit of an ERGM to the data.ergmcontains many other functions as well.
tergmis a collection of extentions toergmenabling it to fit models for dynamic networks.
ergm.countis an extension toergmenabling it to fit models for networks whose relations are counts.
ergm.usertermsprovides a template for implementing new ERGM terms.
snais a set of tools for traditional social network analysis.
degreenetis 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.
latentnetis 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. %% \item \pkg{netperm}: A package for permutation Models for relational %% data. It provides simulation and inference tools for exponential %% families of permutation models on relational structures.
networksisis a package to simulate bipartite graphs with fixed marginals through sequential importance sampling.
releventis a package providing tools to fit relational event models.
networkis a package to create, store, modify and plot the data in network objects. Thenetworkobject class, defined in thenetworkpackage, can represent a range of relational data types and it supports arbitrary vertex / edge /graph attributes. Data stored asnetworkobjects can then be analyzed using all of the component packages in thestatnetsuite.
networkDynamicextendsnetworkwith functionality to store information about about evolution of a network over time, defining anetworkDynamicobject class.
ndtv: (Network Dynamic Temporal Visualization): Exports dynamic network data from networkDynamic objects as animated movies or other representations of relational structure and node attributes that change over time.
EpiModel: Tools for building, solving, and plotting mathematical models of infectious disease, including stochastic models of disease on dynamic networks with demographic processes.

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 Rautomatically 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.

References

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.

Carter T. Butts (2014). {sna: Tools for Social Network Analysis. R package} version 2.3-2. http://CRAN.R-project.org/package=sna

Butts C (2015). {network: Classes for Relational Data}. The Statnet Project (http://www.statnet.org). R package version 1.12.0, CRAN.R-project.org/package=network.

Butts CT (2008). {network: {A} Package for Managing Relational Data in R.} {Journal of Statistical Software}, {24} (2). http://www.jstatsoft.org/v24/i02/.

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 (2008{{b}}). {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 (2003{{b}}). {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 (2003{{a}}). {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 (2003{{b}}). {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 (2008{{b}}). {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. http://dx.doi.org/10.1214/12-EJS696{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.