This statistic implements the cyclical weights statistic, like that defined by Krivitsky (2012), Equation 13, but with the focus dyad being \(y_{j,i}\) rather than \(y_{i,j}\) . For each option, the first (and the default) is more stable but also more conservative, while the second is more sensitive but more likely to induce a multimodal distribution of networks.
# valued: cyclicalweights(twopath="min", combine="max", affect="min")the minimum of the constituent dyads ( "min" ) or their geometric mean
( "geomean" )
the maximum of the
2-path strengths ( "max" ) or their sum ( "sum" )
the minimum of the focus dyad and the
combined strength of the two paths ( "min" ) or their
geometric mean ( "geomean" )
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "cyclicalweights", "subsection")