clustering_tm

Whether a only a subset of 4-paths should we used when calculating the measure. This is particularly useful when running out of memory analysing large networks. If it is set to 1, all the 4-paths are analysed. If it set to a value below one, this is rough

subsample

If a subset of 4-paths is analysed, by setting this parameter, the results are reproducable.

seed

This function calculates the two-mode clusering coefficient as proposed by Opsahl (2010). 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.

Bipartite provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in ecological webs. It focuses on webs consisting of only two trophic levels, e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the webs topology. 

clustering_tm: Redefined clusering coefficient for two-mode networks

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