A function that infers the interaction network using the MRNET algorithm.
matrix of the mutual information.
The MRNET approach starts by selecting the variable Xi having the highest mutual information with the target Y.
Then, it repeatedly enlarges the set of selected variables S by taking the Xk that maximizes
I(Xk;Y) - mean(I(Xk;Xi))
for all Xi already in S.
The procedure stops when the score becomes negative.
By default, the function uses all the available cores. You can
set the actual number of threads used to N by exporting the
A square weighted adjacency matrix of the inferred network.
H. Peng, F.long and C.Ding. Feature selection based on mutual information: Criteria of max-dependency, max relevance and min redundancy. IEEE transaction on Pattern Analysis and Machine Intelligence, 2005.
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