function [ n_data, x, y, r, fxy ]  = bivariate_normal_cdf_values ( n_data )

%*****************************************************************************80
%
%% bivariate_normal_cdf_values() returns some values of the bivariate normal CDF.
%
%  Discussion:
%
%    FXY is the probability that two variables A and B, which are
%    related by a bivariate normal distribution with correlation R,
%    respectively satisfy A <= X and B <= Y.
%
%    Mathematica can evaluate the bivariate normal CDF via the commands:
%
%      <<MultivariateStatistics`
%      cdf = CDF[MultinormalDistribution[{0,0}{{1,r},{r,1}}],{x,y}]
%
%  Licensing:
%
%    This code is distributed under the MIT license.
%
%  Modified:
%
%    23 November 2010
%
%  Author:
%
%    John Burkardt
%
%  Reference:
%
%    National Bureau of Standards,
%    Tables of the Bivariate Normal Distribution and Related Functions,
%    NBS, Applied Mathematics Series, Number 50, 1959.
%
%  Input:
%
%    integer N_DATA.  The user sets N_DATA to 0 before the first call.  
%    Thereafter, it should simply be the value returned by the previous call.
%
%  Output:
%
%    integer N_DATA.  On each call, the routine increments N_DATA by 1, and
%    returns the corresponding data; when there is no more data, the
%    output value of N_DATA will be 0 again.
%
%    real X, Y, the parameters of the function.
%
%    real R, the correlation value.
%
%    real FXY, the value of the function.
%
  n_max = 41;

  fxy_vec(1:n_max) = [ ...
  0.02260327218569867, ...
  0.1548729518584100, ...
  0.4687428083352184, ...
  0.7452035868929476, ...
  0.8318608306874188, ...
  0.8410314261134202, ...
  0.1377019384919464, ...
  0.1621749501739030, ...
  0.1827411243233119, ...
  0.2010067421506235, ...
  0.2177751155265290, ...
  0.2335088436446962, ...
  0.2485057781834286, ...
  0.2629747825154868, ...
  0.2770729823404738, ...
  0.2909261168683812, ...
  0.3046406378726738, ...
  0.3183113449213638, ...
  0.3320262544108028, ...
  0.3458686754647614, ...
  0.3599150462310668, ...
  0.3742210899871168, ...
  0.3887706405282320, ...
  0.4032765198361344, ...
  0.4162100291953678, ...
  0.6508271498838664, ...
  0.8318608306874188, ...
  0.0000000000000000, ...
  0.1666666666539970, ...
  0.2500000000000000, ...
  0.3333333333328906, ...
  0.5000000000000000, ...
  0.7452035868929476, ...
  0.1548729518584100, ...
  0.1548729518584100, ...
  0.06251409470431653, ...
  0.7452035868929476, ...
  0.1548729518584100, ...
  0.1548729518584100, ...
  0.06251409470431653, ...
  0.6337020457912916 ];
  r_vec(1:n_max) = [ ...
     0.500,  0.500,  0.500,  0.500,  0.500, ...
     0.500, -0.900, -0.800, -0.700, -0.600, ...
    -0.500, -0.400, -0.300, -0.200, -0.100, ...
     0.000,  0.100,  0.200,  0.300,  0.400, ...
     0.500,  0.600,  0.700,  0.800,  0.900, ...
     0.673,  0.500, -1.000, -0.500,  0.000, ...
     0.500,  1.000,  0.500,  0.500,  0.500, ...
     0.500,  0.500,  0.500,  0.500,  0.500, ...
     0.500 ];
  x_vec(1:n_max) = [ ...
    -2.0, -1.0,  0.0,  1.0,  2.0, ...
     3.0, -0.2, -0.2, -0.2, -0.2, ...
    -0.2, -0.2, -0.2, -0.2, -0.2, ...
    -0.2, -0.2, -0.2, -0.2, -0.2, ...
    -0.2, -0.2, -0.2, -0.2, -0.2, ...
     1.0,  2.0,  0.0,  0.0,  0.0, ...
     0.0,  0.0,  1.0,  1.0, -1.0, ...
    -1.0,  1.0,  1.0, -1.0, -1.0, ...
     0.7071067811865475  ];
  y_vec(1:n_max) = [ ...
     1.0,  1.0,  1.0,  1.0,  1.0, ...
     1.0,  0.5,  0.5,  0.5,  0.5, ...
     0.5,  0.5,  0.5,  0.5,  0.5, ...
     0.5,  0.5,  0.5,  0.5,  0.5, ...
     0.5,  0.5,  0.5,  0.5,  0.5, ...
     0.5,  1.0,  0.0,  0.0,  0.0, ...
     0.0,  0.0,  1.0, -1.0,  1.0, ...
    -1.0,  1.0, -1.0,  1.0, -1.0, ...
     0.7071067811865475 ];

  if ( n_data < 0 )
    n_data = 0;
  end

  n_data = n_data + 1;

  if ( n_max < n_data )
    n_data = 0;
    r = 0.0;
    x = 0.0;
    y = 0.0;
    fxy = 0.0;
  else
    r = r_vec(n_data);
    x = x_vec(n_data);
    y = y_vec(n_data);
    fxy = fxy_vec(n_data);
  end

  return
end
