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

Description

statnet is a collection of software packages for statistical network analysis that are designed to work together, and provide seamless access to a broad range of network analytic and graphical methodology. 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. Together, the packages provide a comprehensive framework for ERGM-based cross-sectional and dynamic network modeling: tools for model estimation, model evaluation, model-based network simulation, and network visualization. The statistical estimation and simulation functions are based on a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

The code is actively developed and maintained by the statnet development team. New functionality is being added over time.

Details

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:

For data handling:

For analyzing cross-sectional networks:

For temporal (dynamic) network analysis:

Additional utilities:

statnet is a metapackage; its only purpose is to provide a convenient way for a user to load all of the packages in the statnet suite. It does this by depending on all of the packages, so that loading the statnet package into R automatically loads all packages above that are labeled "automatically downloaded". If the user specifies install.packages("statnet",dependencies=T), statnet will also download all of the packages above that are labeled "optional download". Those can, of course, also be installed individually.

Each package in statnet has associated help files and internal documentation, and additional the information can be found on the Statnet Project website (http://statnet.org/). Tutorials, instructions on how to join the statnet help mailing list, references and links to further resources are provided there. For the reference paper(s) that provide information on the theory and methodology behind each specific package use the citation("packagename") function in R after loading statnet.

We have invested much time and effort in creating the statnet suite of packages and supporting material so that others can use and build on these tools. All we ask in return is that you cite it when you use it. For publication of results obtained from statnet, the original authors are to be cited as described in citation("statnet"). If you are only using specific package(s) from the suite, please cite the specific package(s) as described in the appropriate citation("packgename"). Thank you!

Author(s)

Mark S. Handcock handcock@stat.ucla.edu,
David R. Hunter dhunter@stat.psu.edu,
Carter T. Butts buttsc@uci.edu,
Steven M. Goodreau goodreau@uw.edu,
Pavel N. Krivitsky pavel@uow.edu.au, Skye Bender-deMoll skyebend@skyeome.net and
Samuel Jenness (for EpiModel) sjenness@uw.edu Martina Morris morrism@uw.edu

Maintainer: Martina Morris morris@uw.edu


Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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