statnet-package: Easily Install and Load the 'statnet' Packages for...
In statnet: Software Tools for the Statistical Analysis of Network Data

statnet is a collection of software packages for statistical network analysis that are designed to work together, with a common data structure and API, to provide seamless access to a broad range of network analytic and graphical methodology. This package is designed to make it easy to install and load multiple statnet packages in a single step.

statnet software implements recent advances in network modeling based on exponential-family random graph models (ERGM), as well as latent space models and more traditional descriptive network methods. This provides a comprehensive framework for cross-sectional and dynamic network analysis: tools for description, network visualization model estimation, model evaluation, model-based network simulation. The statistical estimation and simulation functions are based on a central Markov chain Monte Carlo (MCMC) algorithm that has been optimized for speed and robustness.

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

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 https://www.r-project.org/ and also on the statnet project website at http://www.statnet.org/

The suite of packages has the following components (those automatically downloaded with the statnet package are noted):

For data handling:

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. (automatically downloaded)
networkDynamic extends network with functionality to store information about about evolution of a network over time, defining a networkDynamic object class. (automatically downloaded)

For analyzing cross-sectional networks:

sna is a set of tools for traditional social network analysis. (automatically downloaded)
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. (automatically downloaded)
ergm.count is an extension to ergm enabling it to fit models for networks whose relations are counts. (automatically downloaded)
ergm.ego is an extension to ergm enabling it to fit models for networks based on egocentrically sampled network data. (separate download required)
ergm.rank is an extension to ergm enabling it to fit models for networks whose relations are ranks. (separate download required)
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. (separate download required)
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. (separate download required)

For temporal (dynamic) network analysis:

tsna is a collection of extensions to sna that provide descriptive summary statistics for temporal networks. (automatically downloaded)
tergm is a collection of extentions to ergm enabling it to fit discrete time models for temporal (dynamic) networks. The main function in tergm is stergm (the “s” stands for separable), which allows the user to specify one ergm for tie formation, and another ergm for tie dissolution. The models can be fit to network panel data, or to a single cross-sectional network with ancillary data on tie duration. (automatically downloaded)
relevent is a package providing tools to fit relational event models. (separate download required)

Additional utilities:

ergm.userterms provides a template for users who want to implement their own new ERGM terms. (separate download required)
networksis is a package to simulate bipartite graphs with fixed marginals through sequential importance sampling. (separate download required)
EpiModel is a package for simulating epidemics (separate download required)

statnet is a metapackage; its only purpose is to provide a convenient way for a user to load the main packages in the statnet suite. 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://www.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. We ask in return 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!

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,
Samuel Jenness (for EpiModel) samuel.m.jenness@emory.edu, and
Martina Morris morrism@uw.edu