For each class, returns lists of all features belonging to subnetworks. (A subnetwork is defined as a collection of features C for which theta[C,!C]==0, and within which no further subnetworks can be identified. In other words, a block in the block diagonal structure of theta, or a set of features that can be connected through theta's edges.)
A list of pXp matrices, each an estimated sparse inverse covariance matrix. (For example, the result of FGL or GGL.)
A list length K, each element of which is a list of subnetworks in class K. Each subnetwork is represented as a vector of feature names.
Patrick Danaher, Pei Wang and Daniela Witten (2011). The joint graphical lasso for inverse covariance estimation across multiple classes. http://arxiv.org/abs/1111.0324
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