function s = std(o, geometric)

% Returns the standard deviation of the variables in a @dseries object o.
% See https://en.wikipedia.org/wiki/Geometric_standard_deviation
%
% INPUTS
% - o             [dseries]   T observations and N variables.
% - geometric     [logical]   if true returns the geometric standard deviation (default is false).
%
% OUTPUTS
% - s             [double]    1*N vector.

% Copyright © 2016-2023 Dynare Team
%
% This file is part of Dynare.
%
% Dynare 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.
%
% Dynare 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 Dynare.  If not, see <https://www.gnu.org/licenses/>.

if nargin<2
    geometric = false;
end

if geometric
    m = mean(o, true);
    s = exp(sqrt(sum(log(bsxfun(@rdivide, o.data, m)).^2, 1)/nobs(o)));
else
    s = std(o.data);
end

return % --*-- Unit tests --*--

%@test:1
% Define a dataset.
A = repmat([1.005, 1.05], 10, 1);

% Instantiate a time series object and compute the mean.
try
   ts = dseries(A);
   s1 = std(ts, true);
   s2 = std(ts);
   t(1) = 1;
catch
   t = 0;
end

if t(1)
   t(2) = dassert(isequal(size(s1),[1, 2]), true);
   t(3) = dassert(isequal(size(s2),[1, 2]), true);
   t(4) = dassert(s1, [1, 1]);
   t(4) = all(abs(s2)<1e-12);
end
T = all(t);
%@eof:1

%@test:2
% Define a dataset.
A = repmat([1.005, 1.05], 10, 1);

% Instantiate a time series object and compute the mean.
try
   ts = dseries(A);
   s1 = ts.std(true);
   s2 = ts.std();
   t(1) = 1;
catch
   t = 0;
end

if t(1)
   t(2) = dassert(isequal(size(s1),[1, 2]), true);
   t(3) = dassert(isequal(size(s2),[1, 2]), true);
   t(4) = dassert(s1, [1, 1]);
   t(4) = all(abs(s2)<1e-12);
end
T = all(t);
%@eof:2

%@test:3
% Define a dataset.
A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]);

% Instantiate time series objects and compute the mean.
try
   ts = dseries(A);
   s = std(ts);
   t(1) = 1;
catch
   t = 0;
end

if t(1)
   t(2) = dassert(isequal(size(s),[1, 2]), true);
   t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true);
end
T = all(t);
%@eof:3

%@test:4
% Define a dataset.
A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]);

% Instantiate time series objects and compute the mean.
try
   ts = dseries(A);
   s = ts.std();
   t(1) = 1;
catch
   t = 0;
end

if t(1)
   t(2) = dassert(isequal(size(s),[1, 2]), true);
   t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true);
end
T = all(t);
%@eof:4
