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COVM generates covariance matrix


function [CC,NN] = covm(X,Y,Mode,W);


 COVM generates covariance matrix
 X and Y can contain missing values encoded with NaN.
 NaN's are skipped, NaN do not result in a NaN output. 
 The output gives NaN only if there are insufficient input data

      calculates the (auto-)correlation matrix of X
      calculates the crosscorrelation between X and Y
    weighted crosscorrelation 

 Mode = 'M' minimum or standard mode [default]
     C = X'*X; or X'*Y correlation matrix

 Mode = 'E' extended mode
     C = [1 X]'*[1 X]; % l is a matching column of 1's
     C is additive, i.e. it can be applied to subsequent blocks and summed up afterwards
     the mean (or sum) is stored on the 1st row and column of C

 Mode = 'D' or 'D0' detrended mode
    the mean of X (and Y) is removed. If combined with extended mode (Mode='DE'), 
     the mean (or sum) is stored in the 1st row and column of C. 
     The default scaling is factor (N-1). 
 Mode = 'D1' is the same as 'D' but uses N for scaling. 

 C = covm(...); 
     C is the scaled by N in Mode M and by (N-1) in mode D.
 [C,N] = covm(...);
    C is not scaled, provides the scaling factor N  
    C./N gives the scaled version. 

 see also: DECOVM, XCOVF


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