/usr/share/octave/packages/3.2/nan-2.4.4/kurtosis.m is in octave-nan 2.4.4-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | 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;
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