--- /dev/null
+## Copyright (C) 2001 Paul Kienzle <pkienzle@users.sf.net>
+##
+## 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/>.
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{v} =} nanstd (@var{X})
+## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt})
+## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt}, @var{dim})
+## Compute the standard deviation while ignoring NaN values.
+##
+## @code{nanstd} is identical to the @code{std} function except that NaN values are
+## ignored. If all values are NaN, the standard deviation is returned as NaN.
+## If there is only a single non-NaN value, the deviation is returned as 0.
+##
+## The argument @var{opt} determines the type of normalization to use. Valid values
+## are
+##
+## @table @asis
+## @item 0:
+## normalizes with @math{N-1}, provides the square root of best unbiased estimator of
+## the variance [default]
+## @item 1:
+## normalizes with @math{N}, this provides the square root of the second moment around
+## the mean
+## @end table
+##
+## The third argument @var{dim} determines the dimension along which the standard
+## deviation is calculated.
+##
+## @seealso{std, nanmin, nanmax, nansum, nanmedian, nanmean}
+## @end deftypefn
+
+function v = nanstd (X, opt, varargin)
+ if nargin < 1
+ print_usage;
+ else
+ if nargin < 3
+ dim = min(find(size(X)>1));
+ if isempty(dim), dim=1; endif;
+ else
+ dim = varargin{1};
+ endif
+ if ((nargin < 2) || isempty(opt))
+ opt = 0;
+ endif
+
+ ## determine the number of non-missing points in each data set
+ n = sum (!isnan(X), varargin{:});
+
+ ## replace missing data with zero and compute the mean
+ X(isnan(X)) = 0;
+ meanX = sum (X, varargin{:}) ./ n;
+
+ ## subtract the mean from the data and compute the sum squared
+ sz = ones(1,length(size(X)));
+ sz(dim) = size(X,dim);
+ v = sumsq (X - repmat(meanX,sz), varargin{:});
+
+ ## because the missing data was set to zero each missing data
+ ## point will contribute (-meanX)^2 to sumsq, so remove these
+ v = v - (meanX .^ 2) .* (size(X,dim) - n);
+
+ if (opt == 0)
+ ## compute the standard deviation from the corrected sumsq using
+ ## max(n-1,1) in the denominator so that the std for a single point is 0
+ v = sqrt ( v ./ max(n - 1, 1) );
+ elseif (opt == 1)
+ ## compute the standard deviation from the corrected sumsq
+ v = sqrt ( v ./ n );
+ else
+ error ("std: unrecognized normalization type");
+ endif
+
+ endif
+endfunction