## Copyright (C) 2012 Rik Wehbring
## Copyright (C) 1995-2016 Kurt Hornik
## Copyright (C) 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{x} =} norminv (@var{p})
## @deftypefnx {statistics} {@var{x} =} norminv (@var{p}, @var{mu})
## @deftypefnx {statistics} {@var{x} =} norminv (@var{p}, @var{mu}, @var{sigma})
##
## Inverse of the normal cumulative distribution function (iCDF).
##
## For each element of @var{p}, compute the quantile (the inverse of the CDF) of
## the normal distribution with mean @var{mu} and standard deviation
## @var{sigma}. The size of @var{p} is the common size of @var{p}, @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, @var{sigma} = 1.
##
## The default values correspond to the standard normal distribution and
## computing its quantile function is also possible with the @code{probit}
## function, which is faster but it does not perform any input validation.
##
## Further information about the normal distribution can be found at
## @url{https://en.wikipedia.org/wiki/Normal_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{normcdf, normpdf, normrnd, normfit, normlike, normstat, probit}
## @end deftypefn

function x = norminv (p, mu, sigma)

  ## Check for valid number of input arguments
  if (nargin < 1)
    error ("norminv: function called with too few input arguments.");
  endif

  ## Add defaults (if missing input arguments)
  if (nargin < 2)
    mu = 0;
  endif
  if (nargin < 3)
    sigma = 1;
  endif

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

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

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

  ## Check for class type
  if (isa (p, 'single') || isa (mu, 'single') || isa (sigma, 'single'))
    x = NaN (size (p), 'single');
  else
    x = NaN (size (p));
  endif

  ## Compute normal iCDF
  if (isscalar (mu) && isscalar (sigma))
    if (isfinite (mu) && (sigma > 0) && (sigma < Inf))
      x = mu + sigma * (-sqrt (2) * erfcinv (2 * p));
    endif
  else
    k = isfinite (mu) & (sigma > 0) & (sigma < Inf);
    x(k) = mu(k) + sigma(k) .* (-sqrt (2) * erfcinv (2 * p(k)));
  endif

endfunction

%!demo
%! ## Plot various iCDFs from the normal distribution
%! p = 0.001:0.001:0.999;
%! x1 = norminv (p, 0, 0.5);
%! x2 = norminv (p, 0, 1);
%! x3 = norminv (p, 0, 2);
%! x4 = norminv (p, -2, 0.8);
%! plot (p, x1, '-b', p, x2, '-g', p, x3, '-r', p, x4, '-c')
%! grid on
%! ylim ([-5, 5])
%! legend ({'μ = 0, σ = 0.5', 'μ = 0, σ = 1', ...
%!          'μ = 0, σ = 2', 'μ = -2, σ = 0.8'}, 'location', 'northwest')
%! title ('Normal iCDF')
%! xlabel ('probability')
%! ylabel ('values in x')

## Test output
%!shared p
%! p = [-1 0 0.5 1 2];
%!assert_equal (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
%!assert_equal (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
%!assert_equal (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
%!assert_equal (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
%!assert_equal (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
%!assert_equal (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
%!assert_equal (norminv (p), probit (p))
%!assert_equal (norminv (0.31254), probit (0.31254))

## Test class of input preserved
%!assert_equal (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
%!assert_equal (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
%!assert_equal (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
%!assert_equal (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))

## Test input validation
%!error<norminv: function called with too few input arguments.> norminv ()
%!error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
%! norminv (ones (3), ones (2), ones (2))
%!error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
%! norminv (ones (2), ones (3), ones (2))
%!error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
%! norminv (ones (2), ones (2), ones (3))
%!error<norminv: P, MU, and SIGMA must be double or single.> norminv (int32 (2), 2, 2)
%!error<norminv: P, MU, and SIGMA must be double or single.> norminv (true, 2, 2)
%!error<norminv: P, MU, and SIGMA must be double or single.> norminv ('a', 2, 2)
%!error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
%!error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
%!error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
