function [ n_data, h, a, t ] = owen_values ( n_data )

%*****************************************************************************80
%
%% OWEN_VALUES returns some values of Owen's T function.
%
%  Discussion:
%
%    Owen's T function is useful for computation of the bivariate normal
%    distribution and the distribution of a skewed normal distribution.
%
%    Although it was originally formulated in terms of the bivariate
%    normal function, the function can be defined more directly as
%
%      T(H,A) = 1 / ( 2 * pi ) *
%        Integral ( 0 <= X <= A ) e^(H^2*(1+X^2)/2) / (1+X^2) dX
%
%    In Mathematica, the function can be evaluated by:
%
%      fx = 1/(2*Pi) * Integrate [ E^(-h^2*(1+x^2)/2)/(1+x^2), {x,0,a} ]
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    10 December 2004
%
%  Author:
%
%    John Burkardt
%
%  Reference:
%
%    Stephen Wolfram,
%    The Mathematica Book,
%    Fourth Edition,
%    Wolfram Media / Cambridge University Press, 1999.
%
%  Parameters:
%
%    Input/output, integer N_DATA.  The user sets N_DATA to 0 before the
%    first call.  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.
%
%    Output, real H, a parameter.
%
%    Output, real A, the upper limit of the integral.
%
%    Output, real T, the value of the function.
%
  n_max = 22;

  a_vec = [ ...
    0.5000000000000000E+00, ...
    0.1000000000000000E+01, ...
    0.2000000000000000E+01, ...
    0.3000000000000000E+01, ...
    0.5000000000000000E+00, ...
    0.1000000000000000E+01, ...
    0.2000000000000000E+01, ...
    0.3000000000000000E+01, ...
    0.5000000000000000E+00, ...
    0.1000000000000000E+01, ...
    0.2000000000000000E+01, ...
    0.3000000000000000E+01, ...
    0.5000000000000000E+00, ...
    0.1000000000000000E+01, ...
    0.2000000000000000E+01, ...
    0.3000000000000000E+01, ...
    0.5000000000000000E+00, ...
    0.1000000000000000E+01, ...
    0.2000000000000000E+01, ...
    0.3000000000000000E+01, ...
    0.1000000000000000E+02, ...
    0.1000000000000000E+03 ];

  h_vec = [ ...
    0.1000000000000000E+01, ...
    0.1000000000000000E+01, ...
    0.1000000000000000E+01, ...
    0.1000000000000000E+01, ...
    0.5000000000000000E+00, ...
    0.5000000000000000E+00, ...
    0.5000000000000000E+00, ...
    0.5000000000000000E+00, ...
    0.2500000000000000E+00, ...
    0.2500000000000000E+00, ...
    0.2500000000000000E+00, ...
    0.2500000000000000E+00, ...
    0.1250000000000000E+00, ...
    0.1250000000000000E+00, ...
    0.1250000000000000E+00, ...
    0.1250000000000000E+00, ...
    0.7812500000000000E-02, ...
    0.7812500000000000E-02, ...
    0.7812500000000000E-02, ...
    0.7812500000000000E-02, ...
    0.7812500000000000E-02, ...
    0.7812500000000000E-02 ];

  t_vec = [ ...
    0.4306469112078537E-01, ...
    0.6674188216570097E-01, ...
    0.7846818699308410E-01, ...
    0.7929950474887259E-01, ...
    0.6448860284750376E-01, ...
    0.1066710629614485E+00, ...
    0.1415806036539784E+00, ...
    0.1510840430760184E+00, ...
    0.7134663382271778E-01, ...
    0.1201285306350883E+00, ...
    0.1666128410939293E+00, ...
    0.1847501847929859E+00, ...
    0.7317273327500385E-01, ...
    0.1237630544953746E+00, ...
    0.1737438887583106E+00, ...
    0.1951190307092811E+00, ...
    0.7378938035365546E-01, ...
    0.1249951430754052E+00, ...
    0.1761984774738108E+00, ...
    0.1987772386442824E+00, ...
    0.2340886964802671E+00, ...
    0.2479460829231492E+00 ];

  if ( n_data < 0 )
    n_data = 0;
  end

  n_data = n_data + 1;

  if ( n_max < n_data )
    n_data = 0;
    h = 0.0;
    a = 0.0;
    t = 0.0;
  else
    h = h_vec(n_data);
    a = a_vec(n_data);
    t = t_vec(n_data);
  end

  return
end
