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 The
Required component packages:
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; andgof, a function to evaluate the goodness
of fit of an ERGM to the data.networkobject
class, defined in thenetworkobjects
can then be analyzed using all of the component packages
in theOptional components, available on CRAN:
Available on request:
The entire 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 library command.
Each package has associated help files and internal documentation that is
supported by the information on the website (
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
statnet: Software tools for the Statistical Modeling of Network Data
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.
For complete citation information, use
citation(package="statnet").
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 network package which allows networks to be
represented in R. The 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 ergm website:
Bender-deMoll S, Morris M, Moody J (2008).
{Prototype Packages for Managing and Animating Longitudinal
Network Data:
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).
{
Butts CT (2007).
{
Butts CT (2008).
{
Butts CT, with help~from David~Hunter, Handcock MS (2007).
{
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
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.
Handcock MS (2003{{b}}).
{
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003{{a}}).
{
Handcock MS, Hunter DR, Butts CT, Goodreau SM, Morris M (2003{{b}}).
{
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}}).
{
Krivitsky PN, Handcock MS (2008).
Fitting Latent Cluster Models for Social Networks with
Krivitsky PN, Handcock MS (2007).
{
Morris M, Handcock MS, Hunter DR (2008).
{Specification of Exponential-Family Random Graph Models:
Terms and Computational Aspects.}
{Journal of Statistical Software}, {24}(4).
Strauss, D., and Ikeda, M.(1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204-212.