Association analysis (Since version 7.16) measures the strengths of associations between sets of nodes or variables X and Y.
For example, it can be used to measure the strength of links in a network.
Supported for discrete, continuous, hybrid (discrete - continuous) and multi-variate nodes.
Mutual information quantifies the amount of information that can be gained from one variable through the other. Often denoted
The Entropy for X and Y, denoted
H(Y) respectively, are reported for convenience, as the normalized forms of the mutual information require them.
Symmetric mutual information
The symmetric mutual information (symmetric uncertainty) is a normalized version of the mutual information. It tells us the strength of the association given the level of uncertainty present in X and Y.
2 * I(X;Y) / (H(X) + H(Y))
The proficiency (uncertainty coefficient) normalizes the mutual information for either X or Y. For example, the proficiency of X given Y tells us how strong the relationship is given that we know Y.
The proficiency of X given Y, denoted