--- /dev/null
+function [C,N,LAGS] = xcovf(X,Y,MAXLAG,SCALEOPT)
+% XCOVF generates cross-covariance function.
+% XCOVF is the same as XCORR except
+% 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
+%
+% [C,N,LAGS] = xcovf(X,MAXLAG,SCALEOPT);
+% calculates the (auto-)correlation function of X
+% [C,N,LAGS] = xcovf(X,Y,MAXLAG,SCALEOPT);
+% calculates the crosscorrelation function between X and Y
+%
+% SCALEOPT [character string] specifies the type of scaling applied
+% to the correlation vector (or matrix). is one of:
+% 'none' return the unscaled correlation, R,
+% 'biased' return the biased average, R/N,
+% 'unbiased' return the unbiassed average, R(k)/(N-|k|),
+% 'coeff' return the correlation coefficient, R/(rms(x).rms(y)),
+% where "k" is the lag, and "N" is the length of X.
+% If omitted, the default value is "none".
+% If Y is supplied but does not have the ame length as X,
+% scale must be "none".
+%
+%
+% see also: COVM, XCORR
+
+% $Id: xcovf.m 9608 2012-02-10 09:56:25Z schloegl $
+% Copyright (C) 2005,2010,2011 by Alois Schloegl <alois.schloegl@gmail.com>
+% This function is part of the NaN-toolbox
+% http://pub.ist.ac.at/~schloegl/matlab/NaN/
+
+% This program is free software; you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation; either version 3 of the License, or
+% (at your option) any later version.
+%
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with this program; If not, see <http://www.gnu.org/licenses/>.
+
+if nargin<2,
+ Y = [];
+ MAXLAG = [];
+ SCALEOPT = 'none';
+elseif ischar(Y),
+ MAXLAG = Y;
+ SCALEOPT=MAXLAG;
+ Y=[];
+elseif all(size(Y)==1),
+ if nargin<3
+ SCALEOPT = 'none';
+ else
+ SCALEOPT = MAXLAG;
+ end;
+ MAXLAG = Y;
+ Y = [];
+end;
+
+if 0,
+
+elseif isempty(Y) && isempty(MAXLAG)
+ NX = isnan(X);
+ X(NX) = 0;
+ [C,LAGS] = xcorr(X,'none');
+ [N,LAGS] = xcorr(1-NX,'none');
+elseif ~isempty(Y) && isempty(MAXLAG)
+ NX = isnan(X);
+ NY = isnan(Y);
+ X(NX) = 0;
+ Y(NY) = 0;
+ [C,LAGS] = xcorr(X,Y,'none');
+ [N,LAGS] = xcorr(1-NX,1-NY,'none');
+elseif isempty(Y) && ~isempty(MAXLAG)
+ NX = isnan(X);
+ X(NX) = 0;
+ [C,LAGS] = xcorr(X,MAXLAG,'none');
+ [N,LAGS] = xcorr(1-NX,MAXLAG,'none');
+elseif ~isempty(Y) && ~isempty(MAXLAG)
+ NX = isnan(X);
+ NY = isnan(Y);
+ X(NX) = 0;
+ Y(NY) = 0;
+ [C,LAGS] = xcorr(X,Y,MAXLAG,'none');
+ [N,LAGS] = xcorr(1-NX,1-NY,MAXLAG,'none');
+end;
+
+if 0,
+
+elseif strcmp(SCALEOPT,'none')
+ % done
+
+elseif strcmp(SCALEOPT,'coeff')
+ ix = find(LAGS==0);
+ if ~any(size(X)==1), %% ~isvector(X)
+ c = C(ix,1:size(X,2)+1:end); %% diagonal elements
+ v = c.^-0.5; % sqrt(1./c(:));
+ v = v'*v;
+ C = C.*repmat(v(:).',size(C,1),1);
+ elseif isempty(Y)
+ C = C/C(ix);
+ else
+ C = C/sqrt(sumsq(X)*sumsq(Y));
+ end;
+
+elseif strcmp(SCALEOPT,'biased')
+ C = C./repmat(max(N),size(C,1),1);
+
+elseif strcmp(SCALEOPT,'unbiased')
+ C = C./(repmat(max(N),size(C,1),1)-repmat(LAGS,1,size(C,2)));
+
+else
+ warning('invalid SCALEOPT - not supported');
+end;
+