## Copyright (C) 2022-2023 Andreas Bertsatos <abertsatos@biol.uoa.gr>
##
## This file is part of the statistics package for GNU Octave.
##
## 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  {statistics} {@var{p} =} evcdf (@var{x})
## @deftypefnx {statistics} {@var{p} =} evcdf (@var{x}, @var{mu})
## @deftypefnx {statistics} {@var{p} =} evcdf (@var{x}, @var{mu}, @var{sigma})
## @deftypefnx {statistics} {@var{p} =} evcdf (@dots{}, @qcode{'upper'})
## @deftypefnx {statistics} {[@var{p}, @var{plo}, @var{pup}] =} evcdf (@var{x}, @var{mu}, @var{sigma}, @var{pcov})
## @deftypefnx {statistics} {[@var{p}, @var{plo}, @var{pup}] =} evcdf (@var{x}, @var{mu}, @var{sigma}, @var{pcov}, @var{alpha})
## @deftypefnx {statistics} {[@var{p}, @var{plo}, @var{pup}] =} evcdf (@dots{}, @qcode{'upper'})
##
## Extreme value cumulative distribution function (CDF).
##
## For each element of @var{x}, compute the cumulative distribution function
## (CDF) of the extreme value distribution (also known as the Gumbel or the type
## I generalized extreme value distribution) at the values in @var{x} with
## location parameter @var{mu} and scale parameter @var{sigma}.  The size of
## @var{p} is the common size of @var{x}, @var{mu} and @var{sigma}.  A scalar
## input functions as a constant matrix of the same size as the other inputs.
##
## Default values are @var{mu} = 0 and @var{sigma} = 1.
##
## When called with three output arguments, i.e. @qcode{[@var{p}, @var{plo},
## @var{pup}]}, @code{evcdf} computes the confidence bounds for @var{p} when the
## input parameters @var{mu} and @var{sigma} are estimates.  In such case,
## @var{pcov}, a @math{2*2} matrix containing the covariance matrix of the
## estimated parameters, is necessary.  Optionally, @var{alpha}, which has a
## default value of 0.05, specifies the @qcode{100 * (1 - @var{alpha})} percent
## confidence bounds.  @var{plo} and @var{pup} are arrays of the same size as
## @var{p} containing the lower and upper confidence bounds.
##
## @code{[@dots{}] = evcdf (@dots{}, "upper")} computes the upper tail
## probability of the extreme value distribution with parameters @var{x0} and
## @var{gamma}, at the values in @var{x}.
##
## The Gumbel distribution is used to model the distribution of the maximum (or
## the minimum) of a number of samples of various distributions.  This version
## is suitable for modeling minima.  For modeling maxima, use the alternative
## Gumbel CDF, @code{gumbelcdf}.
##
## Further information about the Gumbel distribution can be found at
## @url{https://en.wikipedia.org/wiki/Gumbel_distribution}
##
## Input arguments must be @qcode{double} or @qcode{single}; integer, logical,
## and character arrays are rejected.  MATLAB accepts a character array and
## evaluates it at the character codes, which Octave deliberately does not,
## since a character array is an integer type and integers are refused too.
##
## @seealso{evinv, evpdf, evrnd, evfit, evlike, evstat, gumbelcdf}
## @end deftypefn

function [varargout] = evcdf (x, varargin)

  ## Check for valid number of input arguments
  if (nargin < 1 || nargin > 6)
    error ("evcdf: invalid number of input arguments.");
  endif

  ## Check for 'upper' flag
  if (nargin > 1 && strcmpi (varargin{end}, 'upper'))
    uflag = true;
    varargin(end) = [];
  elseif (nargin > 1 && ischar (varargin{end}) && ...
          ! strcmpi (varargin{end}, 'upper'))
    error ("evcdf: invalid argument for upper tail.");
  else
    uflag = false;
  endif

  ## Get extra arguments (if they exist) or add defaults
  if (numel (varargin) > 0)
    mu = varargin{1};
  else
    mu = 0;
  endif
  if (numel (varargin) > 1)
    sigma = varargin{2};
  else
    sigma = 1;
  endif
  if (numel (varargin) > 2)
    pcov = varargin{3};
    ## Check for valid covariance matrix 2x2
    if (! isequal (size (pcov), [2, 2]))
      error ("evcdf: invalid size of covariance matrix.");
    endif
  else
    ## Check that cov matrix is provided if 3 output arguments are requested
    if (nargout > 1)
      error ("evcdf: covariance matrix is required for confidence bounds.");
    endif
    pcov = [];
  endif
  if (numel (varargin) > 3)
    alpha = varargin{4};
    ## Check for valid alpha value
    if (! isnumeric (alpha) || numel (alpha) !=1 || alpha <= 0 || alpha >= 1)
      error ("evcdf: invalid value for alpha.");
    endif
  else
    alpha = 0.05;
  endif

  ## Check for common size of X, MU, and SIGMA
  if (! isscalar (x) || ! isscalar (mu) || ! isscalar (sigma))
    [err, x, mu, sigma] = common_size (x, mu, sigma);
    if (err > 0)
      error ("evcdf: X, MU, and SIGMA must be of common size or scalars.");
    endif
  endif

  ## Check for X, MU, and SIGMA being double or single
  if (! (isfloat (x) && isfloat (mu) && isfloat (sigma)))
    error ("evcdf: X, MU, and SIGMA must be double or single.");
  endif

  ## Check for X, MU, and SIGMA being reals
  if (iscomplex (x) || iscomplex (mu) || iscomplex (sigma))
    error ("evcdf: X, MU, and SIGMA must not be complex.");
  endif

  ## Return NaNs for out of range parameters.
  sigma(sigma <= 0) = NaN;
  ## Compute extreme value cdf
  z = (x - mu) ./ sigma;
  if (uflag)
    p = exp (-exp (z));
  else
    p = -expm1 (-exp (z));
  endif

  ## Check for appropriate class
  if (isa (x, 'single') || isa (mu, 'single') || isa (sigma, 'single'));
    is_class = 'single';
  else
    is_class = 'double';
  endif

  ## Prepare output
  varargout{1} = cast (p, is_class);
  if (nargout > 1)
    plo = NaN (size (z), is_class);
    pup = NaN (size (z), is_class);
  endif

  ## Check sigma
  if (isscalar (sigma))
    if (sigma > 0)
      sigma_p = true (size (z));
    else
      if (nargout == 3)
        varargout{2} = plo;
        varargout{3} = pup;
      endif
      return;
    endif
  else
    sigma_p = sigma > 0;
  endif

  ## Compute confidence bounds (if requested)
  if (nargout >= 2)
   zvar = (pcov(1,1) + 2 * pcov(1,2) * z(sigma_p) + ...
           pcov(2,2) * z(sigma_p) .^ 2) ./ (sigma .^ 2);
   if (any (zvar < 0))
      error ("evcdf: bad covariance matrix.");
   endif
   normz = -probit (alpha / 2);
   halfwidth = normz * sqrt (zvar);
   zlo = z(sigma_p) - halfwidth;
   zup = z(sigma_p) + halfwidth;
   if (uflag)
     plo(sigma_p) = exp (-exp (zup));
     pup(sigma_p) = exp (-exp (zlo));
   else
     plo(sigma_p) = -expm1 (-exp (zlo));
     pup(sigma_p) = -expm1 (-exp (zup));
   endif
   varargout{2} = plo;
   varargout{3} = pup;
  endif

endfunction

%!demo
%! ## Plot various CDFs from the extreme value distribution
%! x = -10:0.01:10;
%! p1 = evcdf (x, 0.5, 2);
%! p2 = evcdf (x, 1.0, 2);
%! p3 = evcdf (x, 1.5, 3);
%! p4 = evcdf (x, 3.0, 4);
%! plot (x, p1, '-b', x, p2, '-g', x, p3, '-r', x, p4, '-c')
%! grid on
%! legend ({'μ = 0.5, σ = 2', 'μ = 1.0, σ = 2', ...
%!          'μ = 1.5, σ = 3', 'μ = 3.0, σ = 4'}, 'location', 'southeast')
%! title ('Extreme value CDF')
%! xlabel ('values in x')
%! ylabel ('probability')

## Test output
%!shared x, y
%! x = [-Inf, 1, 2, Inf];
%! y = [0, 0.6321, 0.9340, 1];
%!assert_equal (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
%!assert_equal (evcdf (x, 1, ones (1,4)), y, 1e-4)
%!assert_equal (evcdf (x, ones (1,4), 1), y, 1e-4)
%!assert_equal (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
%!assert_equal (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
%!assert_equal (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
%!assert_equal (evcdf (x, 'upper'), [1, 0.0660, 0.0006, 0], 1e-4)

## Test class of input preserved
%!assert_equal (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
%!assert_equal (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
%!assert_equal (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
%!assert_equal (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)

## Test input validation
%!error<evcdf: invalid number of input arguments.> evcdf ()
%!error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
%!error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, 'uper')
%!error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
%! evcdf (ones (3), ones (2), ones (2))
%!error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
%!error<evcdf: covariance matrix is required for confidence bounds.> ...
%! [p, plo, pup] = evcdf (1, 2, 3)
%!error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
%! evcdf (1, 2, 3, [1, 0; 0, 1], 0)
%!error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
%! evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
%!error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
%! evcdf (1, 2, 3, [1, 0; 0, 1], 'alpha', 'upper')
%!error<evcdf: X, MU, and SIGMA must be double or single.> evcdf (int32 (2), 2, 2)
%!error<evcdf: X, MU, and SIGMA must be double or single.> evcdf (true, 2, 2)
%!error<evcdf: X, MU, and SIGMA must be double or single.> evcdf ('a', 2, 2)
%!error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
%!error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
%!error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
%!error<evcdf: bad covariance matrix.> ...
%! [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
