as.tnet
(Package: tnet) :
Ensures that networks conform to the tnet stardards
Checks that a network conforms to the tnet stardards, and attaches a label. If the type parameter is not set, the network is assumed to be a binary two-mode network, a weighted one-mode network, or a longitudinal network if there are 2, 3, or 4 columns respectively. Moreover, if a matrix is entered (more than 4 columns and rows), it is assumed to be a weighted one-mode network if square or a two-mode network if non-square.
distance_tm
(Package: tnet) :
Distance in a two-mode network
The shortest path length, or geodesic distance, between two nodes in a binary network is the minimum number of steps you need to make to go from one of them to the other. See the distance_w-function for more details.
This function calculates the generalised clusering coefficient as proposed by Opsahl, T., Panzarasa, P., 2009. Clustering in weighted networks. Social Networks 31 (2), 155-163, doi: 10.1016/j.socnet.2009.02.002 Note: If you are having problems with this function (i.e., run out of memory or it being slow for simulations), there is a quicker and much more memory efficient c++ function. However, this function is not fully integrated in R, and requires a few extra steps. Send me an email to get the source-code and Windows-compiled files.
clustering_tm
(Package: tnet) :
Redefined clusering coefficient for two-mode networks
This function calculates the two-mode clusering coefficient as proposed by Opsahl, T., 2010. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. arXiv:1006.0887. Note: If you are having problems with this function (i.e., run out of memory or it being slow for simulations), there is a quicker and much more memory efficient c++ function. However, this function is not fully integrated in R, and requires a few extra steps. Send me an email to get the source-code and Windows-compiled files.
This function calculates the weighted rich-club coefficient proposed in Opsahl, T., Colizza, V., Panzarasa, P., Ramasco, J.J., 2008. Prominence and control: The weighted rich-club effect. PRL 101. It incorporates two extentions explained in this blog post http://toreopsahl.com/2009/05/29/weighted-rich-club-effect-a-more-appropriate-null-model-for-scientific-collaboration-networks/: 1) a new way of reshuffling (two-mode link reshuffling; 2) calculating significance levels if there are more than 100 random networks (see my PhD thesis; http://toreopsahl.com/publications/thesis/)
This function calculates the local weighted rich-club coefficient proposed in Opsahl, T., 2008. Local weighted rich-club measure. http://toreopsahl.com/2008/12/26/local-weighted-rich-club-measure/
providing 
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