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Tip revision: e3063ee197712ff2ccf516f8df20de1f14e87651 authored by Mark S. Handcock on 20 January 2008, 00:00 UTC
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A Suite of Packages for the Statistical Modeling of Network Data
\pkg{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

\section{Overview of statnet components}{
\pkg{statnet} is written in a combination of 
\R and (ANSI standard) \code{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 \url{} and also
on the \pkg{statnet} project website at \url{}

The \pkg{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: \pkg{ergm} and \pkg{network}

\item \pkg{ergm} is a collection of functions to fit, simulate from, plot and
evaluate exponential random graph models. The main functions within the
\pkg{ergm} package are \code{\link[ergm]{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; \code{simulate}, a function to simulate
random graphs using an ERGM; and \code{\link[ergm]{gof}}, a function to evaluate the goodness
of fit of an ERGM to the data. \pkg{ergm} contains many other functions as
\item \pkg{network} is a package to create, store, modify and plot the data in
network objects. 
The \code{\link[network]{network}} object
class, defined in the \pkg{network} package, 
can represent a range of relational data types and it supports arbitrary
vertex / edge /graph attributes. Data stored as \code{\link[network]{network}} objects
can then be analyzed using all of the component packages
in the \pkg{statnet} suite.

Optional components, available on CRAN: \pkg{sna}, \pkg{degreenet},
\pkg{latentnet}, \pkg{netperm}, \pkg{degreenet} and \pkg{networksis}

\item \pkg{sna}: A set of tools for traditional social network analysis.
\item \pkg{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
\item \pkg{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.
\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.
\item \pkg{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.
\item \pkg{networksis}: A package to simulate bipartite graphs 
with fixed marginals through sequential importance sampling

Available on request: \pkg{dynamicnetwork} and \pkg{rSonia}

\item \pkg{dynamicnetwork}: A set of tools for visualizing dynamically changing
\item \pkg{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

The entire \pkg{statnet} can be installed and/or updated
while in \R using the \code{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 \pkg{statnet} package into \R
automatically loads the \pkg{network} and \pkg{ergm} packages. 
The optional
packages can be loaded 
while in \pkg{statnet} using the \code{\link{library}} command.
Each package has associated help files and internal documentation that is
supported by the information on the website (\url{}).

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 
\emph{statnet: Software tools for the Statistical Modeling of Network Data}

We have invested a lot of time and effort in creating the
\code{statnet} suite of packages for use by other researchers.
lease cite it in all papers where it is used.

For complete citation information, use\cr
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 \code{\link[network]{network}} package which allows networks to be
represented in \R. The \code{\link[ergm]{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 \code{\link[ergm]{ergm}} website:
A tutorial, support newsgroup, references and links to further resources are provided there.
Mark S. Handcock \email{},\cr
David R. Hunter \email{},\cr
Carter T. Butts \email{},\cr
Steven M. Goodreau \email{}, and\cr
Martina Morris \email{}

Maintainer: Mark S. Handcock \email{}
Admiraal R, Handcock MS (2007).
 {\pkg{networksis}: Simulate bipartite graphs with fixed
  marginals through sequential importance sampling}.
 Statnet Project, Seattle, WA.
 Version 1, \url{}.

Bender-deMoll S, Morris M, Moody J (2008).
 {Prototype Packages for Managing and Animating Longitudinal
  Network Data: \pkg{dynamicnetwork} and \pkg{rSoNIA}.}
 {Journal of Statistical Software}, \textbf{24}(7).

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).
 {\pkg{netperm}: Permutation Models for Relational Data}.
 Version 0.2, \url{}.

Butts CT (2007).
 {\pkg{sna}: Tools for Social Network Analysis}.
 Version 1.5, \url{}.

Butts CT (2008).
 {\pkg{network}: {A} Package for Managing Relational Data in \R.}
 {Journal of Statistical Software}, \textbf{24}(2).

Butts CT, with help~from David~Hunter, Handcock MS (2007).
 {\pkg{network}: Classes for Relational Data}.
 Version 1.2, \url{}.

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 \pkg{statnet} Tutorial.}
 {Journal of Statistical Software}, \textbf{24}(8).

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}, \textbf{45}, in press.

Handcock, M. S. (2003)
    \emph{Assessing Degeneracy in Statistical Models of Social Networks},
    Working Paper \#39, 
Center for Statistics and the Social Sciences,
University of Washington.

Handcock MS (2003{{b}}).
 {\pkg{degreenet}: Models for Skewed Count Distributions Relevant
  to Networks}.
 Statnet Project, Seattle, WA.
 Version 1.0, \url{}.

Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003{{a}}).
 {\pkg{ergm}: {A} Package to Fit, Simulate and Diagnose
  Exponential-Family Models for Networks}.
 Statnet Project, Seattle, WA.
 Version 2, \url{}.

Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003{{b}}).
 {\pkg{statnet}: Software Tools for the Statistical Modeling of
  Network Data}.
 Statnet Project, Seattle, WA.
 Version 2, \url{}.

Hunter, D. R. and Handcock, M. S. (2006)
    \emph{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}}).
 {\pkg{ergm}: {A} Package to Fit, Simulate and Diagnose
  Exponential-Family Models for Networks.}
 {Journal of Statistical Software}, \textbf{24}(3).

Krivitsky PN, Handcock MS (2008). 
Fitting Latent Cluster Models for Social Networks with \pkg{latentnet}.
 {Journal of Statistical Software}, \textbf{24}(5).

Krivitsky PN, Handcock MS (2007).
 {\pkg{latentnet}: Latent position and cluster models for
  statistical networks}.
 Seattle, WA.
 Version 2, \url{}.

Morris M, Handcock MS, Hunter DR (2008).
 {Specification of Exponential-Family Random Graph Models:
  Terms and Computational Aspects.}
 {Journal of Statistical Software}, \textbf{24}(4).

Strauss, D., and Ikeda, M.(1990). Pseudolikelihood estimation for social
networks. Journal of the American Statistical Association, 85, 204-212. 
\keyword{ package }
\keyword{ models }
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