% KURT - return kurtosis of input data distribution
%
% Usage:
%   >> k=kurt(data)
%
% Algorithm:
%   Calculates kurtosis or normalized 4th moment of an input data vector
%   Given a matrix, returns a row vector giving the kurtosis' of the columns
%   (Ref: "Numerical Recipes," p. 612)
%
% Author: Martin Mckeown, CNL / Salk Institute, La Jolla, 10/2/96

% Copyright (C) Martin Mckeown, CNL / Salk Institute, La Jolla, 7/1996
%
% This file is part of EEGLAB, see http://www.eeglab.org
% for the documentation and details.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.

% 2/28/97 - made to return separate kurtosis estimates of columns -Scott Makeig
% 01-25-02 reformated help & license, added links -ad 

function [k] = kurt(data)

[r,c]=size(data);
if r==1,
	kdata = data';  % if a row vector, make into a column vector
    r = c;
else
    kdata = data;
end
%fprintf('size of kdata = [%d,%d]\n',size(kdata,1),size(kdata,2));

mn = mean(kdata);              % find the column means
diff = kdata-ones(r,1)*mn;     % remove the column means
dsq = diff.*diff;              % square the data

k =  (sum(dsq.*dsq)./std(kdata).^4)./r - 3;