degreenet-package: Models for Skewed Count Distributions Relevant to Networks

In degreenet: Models for Skewed Count Distributions Relevant to Networks

Models for Skewed Count Distributions Relevant to Networks

Description

degreenet is a collection of functions to fit, diagnose, and simulate from distributions for skewed count data. The coverage of distributions is very selective, focusing on those that have been proposed to model the degree distribution on networks. For the rationale for this choice, see the papers in the references section below. For a list of functions type: help(package='degreenet')

For a complete list of the functions, use library(help="degreenet") or read the rest of the manual. For a simple demonstration, use demo(packages="degreenet").

The degreenet package is part of the statnet suite of packages. The suite was developed to facilitate the statistical analysis of network data.

When publishing results obtained using this package alone see the citation in citation(package="degreenet"). The citation for the original paper to use this package is Handcock and Jones (2003) and it should be cited for the theoretical development.

If you use other packages in the statnet suite, please cite it 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
https://statnet.org. For complete citation information, use
citation(package="statnet").

All programs derived from this or other statnet packages must cite them appropriately.

Details

See the Handcock and Jones (2003) reference (and the papers it cites and is cited by) for more information on the methodology.

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). 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 package relies on the network package which allows networks to be represented in R. The statnet suite of packages allows maximum likelihood estimates of exponential random network models to be calculated using Markov Chain Monte Carlo, as well as a broad range of statistical analysis of networks, such as tools for plotting networks, simulating networks and assessing model goodness-of-fit.

For detailed information on how to download and install the software, go to the statnet website: https://statnet.org. A tutorial, support newsgroup, references and links to further resources are provided there.

Author(s)

Mark S. Handcock [e-mail]

Maintainer: Mark S. Handcock [e-mail]

References

Frank, O., and Strauss, D.(1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842.

Jones, J. H. and Handcock, M. S. (2003). An assessment of preferential attachment as a mechanism for human sexual network formation, Proceedings of the Royal Society, B, 2003, 270, 1123-1128.

Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2003), statnet: Software tools for the Statistical Modeling of Network Data.,
URL https://statnet.org

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

degreenet documentation built on Sept. 26, 2024, 1:08 a.m.