project: Modules required. R is a complete statistics program.
Statnet(http://cran.r-project.org/web/packages/statnet/index.html): An integrated set of tools for the representation, visualization, analysis and simulation of network data. This package For an introduction type: help(package='statnet')
tnet(http://opsahl.co.uk/tnet/) : tnet is a package written in R to serve two purposes:
- Calculate social network measures on weighted datasets
Not everyone is the same. Some people are close to us, whereas others are just acquaintances. Few network measures, and fewer network analysis programmes, can deal with datasets where the ties are differentiated by weights. By removing the weights of relations, we are removing a lot of the richness within the dataset. This means that we are limiting the weight analysis to sensitivity analyses, which are difficult to interpret. A close friendship is not the same as an acquaintance.
- Detect underlying principles that guide tie formation in datasets with time-stamped ties
Network analysis is often based on static networks. In these networks there are issues of dependence as everything depends on everything. Therefore it is difficult to say why certain ties are created and others are not. In networks where the exact sequence of ties is know, the endogeneity issue can be dealt with. This type of data is generally from online communities, email networks, and telephone networks (if your dataset is not like this, but collected in waves, try Siena).
sna(http://cran.r-project.org/web/packages/sna/index.html): A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, p* modeling, random graph generation, and 2D/3D network visualization.