--- /dev/null
+function R=kurtosis(i,DIM)
+% KURTOSIS estimates the kurtosis
+%
+% y = kurtosis(x,DIM)
+% calculates kurtosis of x in dimension DIM
+%
+% DIM dimension
+% 1: STATS of columns
+% 2: STATS of rows
+% default or []: first DIMENSION, with more than 1 element
+%
+% features:
+% - can deal with NaN's (missing values)
+% - dimension argument
+% - compatible to Matlab and Octave
+%
+% see also: SUMSKIPNAN, VAR, STD, VAR, SKEWNESS, MOMENT, STATISTIC,
+% IMPLICIT_SKIP_NAN
+%
+% REFERENCE(S):
+% http://mathworld.wolfram.com/
+
+% 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 2 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/>.
+
+% $Id: kurtosis.m 8223 2011-04-20 09:16:06Z schloegl $
+% Copyright (C) 2000-2003 by Alois Schloegl <alois.schloegl@gmail.com>
+% This function is part of the NaN-toolbox for Octave and Matlab
+% http://pub.ist.ac.at/~schloegl/matlab/NaN/
+
+
+if nargin==1,
+ DIM=min(find(size(i)>1));
+ if isempty(DIM), DIM=1; end;
+end;
+
+[R.SUM,R.N,R.SSQ] = sumskipnan(i,DIM); % sum
+
+R.MEAN = R.SUM./R.N; % mean
+R.SSQ0 = R.SSQ - real(R.SUM).*real(R.MEAN) - imag(R.SUM).*imag(R.MEAN); % sum square with mean removed
+
+%if flag_implicit_unbiased_estim; %% ------- unbiased estimates -----------
+ n1 = max(R.N-1,0); % in case of n=0 and n=1, the (biased) variance, STD and SEM are INF
+%else
+% n1 = R.N;
+%end;
+
+R.VAR = R.SSQ0./n1; % variance (unbiased)
+%R.STD = sqrt(R.VAR); % standard deviation
+
+i = i - repmat(R.MEAN,size(i)./size(R.MEAN));
+%R.CM3 = sumskipnan(i.^3,DIM)./n1;
+R.CM4 = sumskipnan(i.^4,DIM)./n1;
+
+%R.SKEWNESS = R.CM3./(R.STD.^3);
+R = R.CM4./(R.VAR.^2)-3;