## Copyright (C) 2012  Arno Onken
##
## 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{r} = copularnd (@var{family}, @var{theta}, @var{n})
## @deftypefnx {Function File} @var{r} = copularnd (@var{family}, @var{theta}, @var{n}, @var{d})
## @deftypefnx {Function File} @var{r} = copularnd ('t', @var{theta}, @var{df}, @var{n})
##
## Random arrays from the copula family distributions.
##
## @subheading Arguments
##
## @itemize @bullet
## @item
## @var{family} is the copula family name. Currently, @var{family} can be
## @code{'Gaussian'} for the Gaussian family, @code{'t'} for the Student's t
## family, or @code{'Clayton'} for the Clayton family.
##
## @item
## @var{theta} is the parameter of the copula. For the Gaussian and Student's t
## copula, @var{theta} must be a correlation matrix. For bivariate copulas
## @var{theta} can also be a correlation coefficient. For the Clayton family,
## @var{theta} must be a vector with the same number of elements as samples to
## be generated or be scalar.
##
## @item
## @var{df} is the degrees of freedom for the Student's t family. @var{df} must
## be a vector with the same number of elements as samples to be generated or
## be scalar.
##
## @item
## @var{n} is the number of rows of the matrix to be generated. @var{n} must be
## a non-negative integer and corresponds to the number of samples to be
## generated.
##
## @item
## @var{d} is the number of columns of the matrix to be generated. @var{d} must
## be a positive integer and corresponds to the dimension of the copula.
## @end itemize
##
## @subheading Return values
##
## @itemize @bullet
## @item
## @var{r} is a matrix of random samples from the copula with @var{n} samples
## of distribution dimension @var{d}.
## @end itemize
##
## @subheading Examples
##
## @example
## @group
## theta = 0.5;
## r = copularnd ("Gaussian", theta);
## @end group
##
## @group
## theta = 0.5;
## df = 2;
## r = copularnd ("t", theta, df);
## @end group
##
## @group
## theta = 0.5;
## n = 2;
## r = copularnd ("Clayton", theta, n);
## @end group
## @end example
##
## @subheading References
##
## @enumerate
## @item
## Roger B. Nelsen. @cite{An Introduction to Copulas}. Springer, New York,
## second edition, 2006.
## @end enumerate
## @end deftypefn

function r = copularnd (family, theta, df, n)

  ## Check arguments
  if (nargin < 2)
    print_usage ();
  endif

  if (! ischar (family))
    error ("copularnd: family must be one of 'Gaussian', 't', and 'Clayton'.");
  endif

  lower_family = lower (family);

  ## Check family and copula parameters
  switch (lower_family)

    case {"gaussian"}
      ## Gaussian family
      if (isscalar (theta))
        ## Expand a scalar to a correlation matrix
        theta = [1, theta; theta, 1];
      endif
      if (! ismatrix (theta) || any (diag (theta) != 1) || ...
          any (any (theta != theta')) || min (eig (theta)) <= 0)
        error ("copularnd: THETA must be a correlation matrix.");
      endif
      if (nargin > 3)
        d = n;
        if (! isscalar (d) || d != size (theta, 1))
          error ("copularnd: D must correspond to dimension of theta.");
        endif
      else
        d = size (theta, 1);
      endif
      if (nargin < 3)
        n = 1;
      else
        n = df;
        if (! isscalar (n) || (n < 0) || round (n) != n)
          error ("copularnd: N must be a non-negative integer.");
        endif
      endif

    case {"t"}
      ## Student's t family
      if (nargin < 3)
        print_usage ();
      endif
      if (isscalar (theta))
        ## Expand a scalar to a correlation matrix
        theta = [1, theta; theta, 1];
      endif
      if (! ismatrix (theta) || any (diag (theta) != 1) || ...
          any (any (theta != theta')) || min (eig (theta)) <= 0)
        error ("copularnd: THETA must be a correlation matrix.");
      endif
      if (! isscalar (df) && (! isvector (df) || length (df) != n))
        error (strcat (["copularnd: DF must be a vector with the same"], ...
                       [" number of rows as r or be scalar."]));
      endif
      df = df(:);
      if (nargin < 4)
        n = 1;
      else
        if (! isscalar (n) || (n < 0) || round (n) != n)
          error ("copularnd: N must be a non-negative integer.");
        endif
      endif

    case {"clayton"}
      ## Archimedian one parameter family
      if (nargin < 4)
        ## Default is bivariate
        d = 2;
      else
        d = n;
        if (! isscalar (d) || (d < 2) || round (d) != d)
          error ("copularnd: D must be an integer greater than 1.");
        endif
      endif
      if (nargin < 3)
        ## Default is one sample
        n = 1;
      else
        n = df;
        if (! isscalar (n) || (n < 0) || round (n) != n)
          error ("copularnd: N must be a non-negative integer.");
        endif
      endif
      if (! isvector (theta) || (! isscalar (theta) && size (theta, 1) != n))
        error (strcaty (["copularnd: THETA must be a column vector with"], ...
                        [" the number of rows equal to N or be scalar."]));
      endif
      if (n > 1 && isscalar (theta))
        theta = repmat (theta, n, 1);
      endif

    otherwise
      error ("copularnd: unknown copula family '%s'.", family);

  endswitch

  if (n == 0)
    ## Input is empty
    r = zeros (0, d);
  else

    ## Draw random samples according to family
    switch (lower_family)

      case {"gaussian"}
        ## The Gaussian family
        r = normcdf (mvnrnd (zeros (1, d), theta, n), 0, 1);
        ## No parameter bounds check
        k = [];

      case {"t"}
        ## The Student's t family
        r = tcdf (mvtrnd (theta, df, n), df);
        ## No parameter bounds check
        k = [];

      case {"clayton"}
        ## The Clayton family
        u = rand (n, d);
        if (d == 2)
          r = zeros (n, 2);
          ## Conditional distribution method for the bivariate case which also
          ## works for theta < 0
          r(:, 1) = u(:, 1);
          r(:, 2) = (1 + u(:, 1) .^ (-theta) .* (u(:, 2) .^ ...
                    (-theta ./ (1 + theta)) - 1)) .^ (-1 ./ theta);
        else
          ## Apply the algorithm by Marshall and Olkin:
          ## Frailty distribution for Clayton copula is gamma
          y = randg (1 ./ theta, n, 1);
          r = (1 - log (u) ./ repmat (y, 1, d)) .^ (-1 ./ repmat (theta, 1, d));
        endif
        k = find (theta == 0);
        if (any (k))
          ## Product copula at columns k
          r(k, :) = u(k, :);
        endif
        ## Continue argument check
        if (d == 2)
          k = find (! (theta >= -1) | ! (theta < inf));
        else
          k = find (! (theta >= 0) | ! (theta < inf));
        endif

    endswitch

    ## Out of bounds parameters
    if (any (k))
      r(k, :) = NaN;
    endif

  endif

endfunction

%!test
%! theta = 0.5;
%! r = copularnd ("Gaussian", theta);
%! assert (size (r), [1, 2]);
%! assert (all ((r >= 0) & (r <= 1)));

%!test
%! theta = 0.5;
%! df = 2;
%! r = copularnd ("t", theta, df);
%! assert (size (r), [1, 2]);
%! assert (all ((r >= 0) & (r <= 1)));

%!test
%! theta = 0.5;
%! r = copularnd ("Clayton", theta);
%! assert (size (r), [1, 2]);
%! assert (all ((r >= 0) & (r <= 1)));

%!test
%! theta = 0.5;
%! n = 2;
%! r = copularnd ("Clayton", theta, n);
%! assert (size (r), [n, 2]);
%! assert (all ((r >= 0) & (r <= 1)));

%!test
%! theta = [1; 2];
%! n = 2;
%! d = 3;
%! r = copularnd ("Clayton", theta, n, d);
%! assert (size (r), [n, d]);
%! assert (all ((r >= 0) & (r <= 1)));
