# include <cmath>
# include <cstdlib>
# include <cstring>
# include <iomanip>
# include <iostream>

using namespace std;

# include "ranlib.hpp"
# include "rnglib.hpp"

//****************************************************************************80

char ch_cap ( char ch )

//****************************************************************************80
//
//  Purpose:
//
//    CH_CAP capitalizes a single character.
//
//  Discussion:
//
//    This routine should be equivalent to the library "toupper" function.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 July 1998
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, char CH, the character to capitalize.
//
//    Output, char CH_CAP, the capitalized character.
//
{
  if ( 97 <= ch && ch <= 122 )
  {
    ch = ch - 32;
  }
  return ch;
}
//****************************************************************************80

float genbet ( float aa, float bb )

//****************************************************************************80
//
//  Purpose:
//
//    GENBET generates a beta random deviate.
//
//  Discussion:
//
//    This procedure returns a single random deviate from the beta distribution
//    with parameters A and B.  The density is
//
//      x^(a-1) * (1-x)^(b-1) / Beta(a,b) for 0 < x < 1
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2014
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Russell Cheng,
//    Generating Beta Variates with Nonintegral Shape Parameters,
//    Communications of the ACM,
//    Volume 21, Number 4, April 1978, pages 317-322.
//
//  Parameters:
//
//    Input, float AA, the first parameter of the beta distribution.
//    0.0 < AA.
//
//    Input, float BB, the second parameter of the beta distribution.
//    0.0 < BB.
//
//    Output, float GENBET, a beta random variate.
//
{
  float a;
  float alpha;
  float b;
  float beta;
  float delta;
  float gamma;
  float k1;
  float k2;
  const double log4 = 1.3862943611198906188;
  const double log5 = 1.6094379124341003746;
  float r;
  float s;
  float t;
  float u1;
  float u2;
  float v;
  float value;
  float w;
  float y;
  float z;

  if ( aa <= 0.0 )
  {
    cerr << "\n";
    cerr << "GENBET - Fatal error!\n";
    cerr << "  AA <= 0.0\n";
    exit ( 1 );
  }

  if ( bb <= 0.0 ) 
  {
    cerr << "\n";
    cerr << "GENBET - Fatal error!\n";
    cerr << "  BB <= 0.0\n";
    exit ( 1 );
  }
//
//  Algorithm BB
//
  if ( 1.0 < aa && 1.0 < bb )
  {
    a = fmin ( aa, bb );
    b = fmax ( aa, bb );
    alpha = a + b;
    beta = sqrt ( ( alpha - 2.0 ) / ( 2.0 * a * b - alpha ) );
    gamma = a + 1.0 / beta;

    for ( ; ; )
    {
      u1 = r4_uni_01 ( );
      u2 = r4_uni_01 ( );
      v = beta * log ( u1 / ( 1.0 - u1 ) );
//
//  exp ( v ) replaced by r4_exp ( v );
//
      w = a * r4_exp ( v );

      z = u1 * u1 * u2;
      r = gamma * v - log4;
      s = a + r - w;

      if ( 5.0 * z <= s + 1.0 + log5 )
      {
        break;
      }

      t = log ( z );
      if ( t <= s )
      {
        break;
      }

      if ( t <= ( r + alpha * log ( alpha / ( b + w ) ) ) )
      {
        break;
      }
    }
  }
//
//  Algorithm BC
//
  else
  {
    a = fmax ( aa, bb );
    b = fmin ( aa, bb );
    alpha = a + b;
    beta = 1.0 / b;
    delta = 1.0 + a - b;
    k1 = delta * ( 1.0 / 72.0 + b / 24.0 ) 
      / ( a / b - 7.0 / 9.0 );
    k2 = 0.25 + ( 0.5 + 0.25 / delta ) * b;

    for ( ; ; )
    {
      u1 = r4_uni_01 ( );
      u2 = r4_uni_01 ( );

      if ( u1 < 0.5 )
      {
        y = u1 * u2;
        z = u1 * y;

        if ( k1 <= 0.25 * u2 + z - y )
        {
          continue;
        }
      }
      else
      {
        z = u1 * u1 * u2;

        if ( z <= 0.25 )
        {
          v = beta * log ( u1 / ( 1.0 - u1 ) );
          w = a * exp ( v );

          if ( aa == a )
          {
            value = w / ( b + w );
          }
          else
          {
            value = b / ( b + w );
          }
          return value;
        }

        if ( k2 < z )
        {
          continue;
        }
      }

      v = beta * log ( u1 / ( 1.0 - u1 ) );
      w = a * exp ( v );

      if ( log ( z ) <= 
        alpha * ( log ( alpha / ( b + w ) ) + v ) - log4 )
      {
        break;
      }
    }
  }

  if ( aa == a )
  {
    value = w / ( b + w );
  }
  else
  {
    value = b / ( b + w );
  }
  return value;
}
//****************************************************************************80

float genchi ( float df )

//****************************************************************************80
//
//  Purpose:
//
//    GENCHI generates a Chi-Square random deviate.
//
//  Discussion:
//
//    This procedure generates a random deviate from the chi square distribution
//    with DF degrees of freedom random variable.
//
//    The algorithm exploits the relation between chisquare and gamma.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float DF, the degrees of freedom.
//    0.0 < DF.
//
//    Output, float GENCHI, a random deviate from the distribution.
//
{
  float arg1;
  float arg2;
  float value;

  if ( df <= 0.0 )
  {
    cerr << "\n";
    cerr << "GENCHI - Fatal error!\n";
    cerr << "  DF <= 0.\n";
    cerr << "  Value of DF: " << df << "\n";
    exit ( 1 );
  }

  arg1 = 1.0;
  arg2 = df / 2.0;

  value = 2.0 * gengam ( arg1, arg2 );

  return value;
}
//****************************************************************************80

float genexp ( float av )

//****************************************************************************80
//
//  Purpose:
//
//    GENEXP generates an exponential random deviate.
//
//  Discussion:
//
//    This procedure generates a single random deviate from an exponential
//    distribution with mean AV.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Computer Methods for Sampling From the
//    Exponential and Normal Distributions,
//    Communications of the ACM,
//    Volume 15, Number 10, October 1972, pages 873-882.
//
//  Parameters:
//
//    Input, float AV, the mean of the exponential distribution 
//    from which a random deviate is to be generated.
//
//    Output, float GENEXP, a random deviate from the distribution.
//
{
  float value;

  value = sexpo ( ) * av;

  return value;
}
//****************************************************************************80

float genf ( float dfn, float dfd )

//****************************************************************************80
//
//  Purpose:
//
//    GENF generates an F random deviate.
//
//  Discussion:
//
//    This procedure generates a random deviate from the F (variance ratio)
//    distribution with DFN degrees of freedom in the numerator
//    and DFD degrees of freedom in the denominator.
//
//    It directly generates the ratio of chisquare variates
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float DFN, the numerator degrees of freedom.
//    0.0 < DFN.
//
//    Input, float DFD, the denominator degrees of freedom.
//    0.0 < DFD.
//
//    Output, float GENF, a random deviate from the distribution.
//
{
  float value;
  float xden;
  float xnum;

  if ( dfn <= 0.0 )
  {
    cerr << "\n";
    cerr << "GENF - Fatal error!\n";
    cerr << "  DFN <= 0.0\n";
    exit ( 1 );
  }

  if ( dfd <= 0.0 )
  {
    cerr << "\n";
    cerr << "GENF - Fatal error!\n";
    cerr << "  DFD <= 0.0\n";
    exit ( 1 );
  }

  xnum = genchi ( dfn ) / dfn;
  xden = genchi ( dfd ) / dfd;
  value = xnum / xden;

  return value;
}
//****************************************************************************80

float gengam ( float a, float r )

//****************************************************************************80
//
//  Purpose:
//
//    GENGAM generates a Gamma random deviate.
//
//  Discussion:
//
//    This procedure generates random deviates from the gamma distribution whose
//    density is (A^R)/Gamma(R) * X^(R-1) * Exp(-A*X)
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Generating Gamma Variates by a Modified Rejection Technique,
//    Communications of the ACM,
//    Volume 25, Number 1, January 1982, pages 47-54.
//
//    Joachim Ahrens, Ulrich Dieter,
//    Computer Methods for Sampling from Gamma, Beta, Poisson and
//    Binomial Distributions,
//    Computing,
//    Volume 12, Number 3, September 1974, pages 223-246.
//
//  Parameters:
//
//    Input, float A, the location parameter.
//
//    Input, float R, the shape parameter.
//
//    Output, float GENGAM, a random deviate from the distribution.
//
{
  float value;

  value = sgamma ( r ) / a;

  return value;
}
//****************************************************************************80

float *genmn ( float parm[] )

//****************************************************************************80
//
//  Purpose:
//
//    GENMN generates a multivariate normal deviate.
//
//  Discussion:
//
//    The method is:
//    1) Generate P independent standard normal deviates - Ei ~ N(0,1)
//    2) Using Cholesky decomposition find A so that A'*A = COVM
//    3) A' * E + MEANV ~ N(MEANV,COVM)
//
//    Note that PARM contains information needed to generate the
//    deviates, and is set up by SETGMN.
//
//    PARM(1) contains the size of the deviates, P
//    PARM(2:P+1) contains the mean vector.
//    PARM(P+2:P*(P+3)/2+1) contains the upper half of the Cholesky
//    decomposition of the covariance matrix.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float PARM[P*(P+3)/2+1], parameters set by SETGMN.!
//
//    Output, float GENMN[P], a random deviate from the distribution.
//
{
  float ae;
  int i;
  int icount;
  int j;
  int p;
  float *work;
  float *x;

  p = ( int ) ( parm[0] );
//
//  Generate P independent normal deviates.
//
  work = new float[p];

  for ( i = 0; i < p; i++ )
  {
    work[i] = snorm ( );
  }
//
//  Compute X = MEANV + A' * WORK
//
  x = new float[p];

  for ( i = 0; i < p; i++ )
  {
    icount = 0;
    ae = 0.0;
    for ( j = 0; j <= i; j++ )
    {
      icount = icount + j;
      ae = ae + parm[i+j*p-icount+p+1] * work[j];
    }
    x[i] = ae + parm[i+1];
  }

  delete [] work;

  return x;
}
//****************************************************************************80

int *genmul ( int n, float p[], int ncat )

//****************************************************************************80
//
//  Purpose:
//
//    GENMUL generates a multinomial random deviate.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Luc Devroye,
//    Non-Uniform Random Variate Generation,
//    Springer, 1986,
//    ISBN: 0387963057,
//    LC: QA274.D48.
//
//  Parameters:
//
//    Input, int N, the number of events, which will be
//    classified into one of the NCAT categories.
//
//    Input, float P[NCAT-1].  P(I) is the probability that an event
//    will be classified into category I.  Thus, each P(I) must be between 
//    0.0 and 1.0.  Only the first NCAT-1 values of P must be defined since 
//    P(NCAT) would be 1.0 minus the sum of the first NCAT-1 P's.
//
//    Input, int NCAT, the number of categories.
//
//    Output, int GENMUL[NCAT], a random observation from 
//    the multinomial distribution.  All IX(i) will be nonnegative and their 
//    sum will be N.
//
{
  int i;
  int icat;
  int *ix;
  int ntot;
  float prob;
  float ptot;

  if ( n < 0 )
  {
    cerr << "\n";
    cerr << "GENMUL - Fatal error!\n";
    cerr << "  N < 0\n";
    exit ( 1 );
  }

  if ( ncat <= 1 )
  {
    cerr << "\n";
    cerr << "GENMUL - Fatal error!\n";
    cerr << "  NCAT <= 1\n";
    exit ( 1 );
  }

  for ( i = 0; i < ncat - 1; i++ )
  {
    if ( p[i] < 0.0 )
    {
      cerr << "\n";
      cerr << "GENMUL - Fatal error!\n";
      cerr << "  Some P(i) < 0.\n";
      exit ( 1 );
    }

    if ( 1.0 < p[i] )
    {
      cerr << "\n";
      cerr << "GENMUL - Fatal error!\n";
      cerr << "  Some 1 < P(i).\n";
      exit ( 1 );
    }
  }

  ptot = 0.0;
  for ( i = 0; i < ncat - 1; i++ )
  {
    ptot = ptot + p[i];
  }

  if ( 0.99999 < ptot ) 
  {
    cerr << "\n";
    cerr << "GENMUL - Fatal error!\n";
    cerr << "  1 < Sum of P().\n";
    exit ( 1 );
  }
//
//  Initialize variables.
//
  ntot = n;
  ptot = 1.0;

  ix = new int[ncat];
  for ( i = 0; i < ncat; i++ )
  {
    ix[i] = 0;
  }
//
//  Generate the observation.
//
  for ( icat = 0; icat < ncat - 1; icat++ )
  {
    prob = p[icat] / ptot;
    ix[icat] = ignbin ( ntot, prob );
    ntot = ntot - ix[icat];
    if ( ntot <= 0 )
    {
      return ix;
    }
    ptot = ptot - p[icat];
  }

  ix[ncat-1] = ntot;

  return ix;
}
//****************************************************************************80

float gennch ( float df, float xnonc )

//****************************************************************************80
//
//  Purpose:
//
//    GENNCH generates a noncentral Chi-Square random deviate.
//
//  Discussion:
//
//    This procedure generates a random deviate from the  distribution of a
//    noncentral chisquare with DF degrees of freedom and noncentrality parameter
//    XNONC.
//
//    It uses the fact that the noncentral chisquare is the sum of a chisquare
//    deviate with DF-1 degrees of freedom plus the square of a normal
//    deviate with mean XNONC and standard deviation 1.
//
//    A subtle ambiguity arises in the original formulation:
//
//      gennch = genchi ( arg1 ) + ( gennor ( arg2, arg3 ) ) ** 2
//
//    because the compiler is free to invoke either genchi or gennor
//    first, both of which alter the random number generator state,
//    resulting in two distinct possible results.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float DF, the degrees of freedom.
//    1.0 < DF.
//
//    Input, float XNONC, the noncentrality parameter.
//    0.0 <= XNONC.
//
//    Output, float GENNCH, a random deviate from the distribution.
//
{
  float arg1;
  float arg2;
  float arg3;
  float t1;
  float t2;
  float value;

  if ( df <= 1.0 )
  {
    cerr << "\n";
    cerr << "GENNCH - Fatal error!\n";
    cerr << "  DF <= 1.\n";
    exit ( 1 );
  }

  if ( xnonc < 0.0 )
  {
    cerr << "\n";
    cerr << "GENNCH - Fatal error!\n";
    cerr << "  XNONC < 0.0.\n";
    exit ( 1 );
  }

  arg1 = df - 1.0;
  arg2 = sqrt ( xnonc );
  arg3 = 1.0;

  t1 = genchi ( arg1 );
  t2 = gennor ( arg2, arg3 );

  value = t1 + t2 * t2;

  return value;
}
//****************************************************************************80

float gennf ( float dfn, float dfd, float xnonc )

//****************************************************************************80
//
//  Purpose:
//
//    GENNF generates a noncentral F random deviate.
//
//  Discussion:
//
//    This procedure generates a random deviate from the noncentral F
//    (variance ratio) distribution with DFN degrees of freedom in the
//    numerator, and DFD degrees of freedom in the denominator, and
//    noncentrality parameter XNONC.
//
//    It directly generates the ratio of noncentral numerator chisquare variate
//    to central denominator chisquare variate.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float DFN, the numerator degrees of freedom.
//    1.0 < DFN.
//
//    Input, float DFD, the denominator degrees of freedom.
//    0.0 < DFD.
//
//    Input, float XNONC, the noncentrality parameter.
//    0.0 <= XNONC.
//
//    Output, float GENNF, a random deviate from the distribution.
//
{
  float value;
  float xden;
  float xnum;

  if ( dfn <= 1.0 )
  {
    cerr << "\n";
    cerr << "GENNF - Fatal error!\n";
    cerr << "  DFN <= 1.0\n";
    exit ( 1 );
  }

  if ( dfd <= 0.0 )
  {
    cerr << "\n";
    cerr << "GENNF - Fatal error!\n";
    cerr << "  DFD <= 0.0\n";
    exit ( 1 );
  }

  if ( xnonc < 0.0 )
  {
    cerr << "\n";
    cerr << "GENNF - Fatal error!\n";
    cerr << "  XNONC < 0.0\n";
    exit ( 1 );
  }

  xnum = gennch ( dfn, xnonc ) / dfn;
  xden = genchi ( dfd ) / dfd;

  value = xnum / xden;

  return value;
}
//****************************************************************************80

float gennor ( float av, float sd )

//****************************************************************************80
//
//  Purpose:
//
//    GENNOR generates a normal random deviate.
//
//  Discussion:
//
//    This procedure generates a single random deviate from a normal distribution
//    with mean AV, and standard deviation SD.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Extensions of Forsythe's Method for Random
//    Sampling from the Normal Distribution,
//    Mathematics of Computation,
//    Volume 27, Number 124, October 1973, page 927-937.
//
//  Parameters:
//
//    Input, float AV, the mean.
//
//    Input, float SD, the standard deviation.
//
//    Output, float GENNOR, a random deviate from the distribution.
//
{
  float value;

  value = sd * snorm ( ) + av;

  return value;
}
//****************************************************************************80

void genprm ( int iarray[], int n )

//****************************************************************************80
//
//  Purpose:
//
//    GENPRM generates and applies a random permutation to an array.
//
//  Discussion:
//
//    To see the permutation explicitly, let the input array be
//    1, 2, ..., N.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input/output, int IARRAY(N), an array to be permuted.
//
//    Input, int N, the number of entries in the array.
//
{
  int i;
  int itmp;
  int iwhich;

  for ( i = 1; i <= n; i++ )
  {
    iwhich = ignuin ( i, n );
    itmp = iarray[iwhich-1];
    iarray[iwhich-1] = iarray[i-1];
    iarray[i-1] = itmp;
  }
  return;
}
//****************************************************************************80

float genunf ( float low, float high )

//****************************************************************************80
//
//  Purpose:
//
//    GENUNF generates a uniform random deviate.
//
//  Discussion:
//
//    This procedure generates a real deviate uniformly distributed between
//    LOW and HIGH.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float LOW, HIGH, the lower and upper bounds.
//
//    Output, float GENUNF, a random deviate from the distribution.
//
{
  float value;

  value = low + ( high - low ) * r4_uni_01 ( );

  return value;
}
//****************************************************************************80

int i4_max ( int i1, int i2 )

//****************************************************************************80
//
//  Purpose:
//
//    I4_MAX returns the maximum of two I4's.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    29 August 2006
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int I1, I2, are two integers to be compared.
//
//    Output, int I4_MAX, the larger of I1 and I2.
//
{
  int value;

  if ( i2 < i1 )
  {
    value = i1;
  }
  else
  {
    value = i2;
  }
  return value;
}
//****************************************************************************80

int i4_min ( int i1, int i2 )

//****************************************************************************80
//
//  Purpose:
//
//    I4_MIN returns the smaller of two I4's.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    29 August 2006
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, int I1, I2, two integers to be compared.
//
//    Output, int I4_MIN, the smaller of I1 and I2.
//
{
  int value;

  if ( i1 < i2 )
  {
    value = i1;
  }
  else
  {
    value = i2;
  }
  return value;
}
//****************************************************************************80

int ignbin ( int n, float pp )

//****************************************************************************80
//
//  Purpose:
//
//    IGNBIN generates a binomial random deviate.
//
//  Discussion:
//
//    This procedure generates a single random deviate from a binomial
//    distribution whose number of trials is N and whose
//    probability of an event in each trial is P.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Voratas Kachitvichyanukul, Bruce Schmeiser,
//    Binomial Random Variate Generation,
//    Communications of the ACM,
//    Volume 31, Number 2, February 1988, pages 216-222.
//
//  Parameters:
//
//    Input, int N, the number of binomial trials, from which a
//    random deviate will be generated.
//    0 < N.
//
//    Input, float PP, the probability of an event in each trial of
//    the binomial distribution from which a random deviate is to be generated.
//    0.0 < PP < 1.0.
//
//    Output, int IGNBIN, a random deviate from the
//    distribution.
//
{
  float al;
  float alv;
  float amaxp;
  float c;
  float f;
  float f1;
  float f2;
  float ffm;
  float fm;
  float g;
  int i;
  int ix;
  int ix1;
  int k;
  int m;
  int mp;
  float p;
  float p1;
  float p2;
  float p3;
  float p4;
  float q;
  float qn;
  float r;
  float t;
  float u;
  float v;
  int value;
  float w;
  float w2;
  float x;
  float x1;
  float x2;
  float xl;
  float xll;
  float xlr;
  float xm;
  float xnp;
  float xnpq;
  float xr;
  float ynorm;
  float z;
  float z2;

  if ( pp <= 0.0 || 1.0 <= pp )
  {
    cerr << "\n";
    cerr << "IGNBIN - Fatal error!\n";
    cerr << "  PP is out of range.\n";
    exit ( 1 );
  }

  p = fmin ( pp, 1.0 - pp );
  q = 1.0 - p;
  xnp = ( float ) ( n ) * p;

  if ( xnp < 30.0 )
  {
    qn = pow ( q, n );
    r = p / q;
    g = r * ( float ) ( n + 1 );

    for ( ; ; )
    {
      ix = 0;
      f = qn;
      u = r4_uni_01 ( );

      for ( ; ; )
      {
        if ( u < f )
        {
          if ( 0.5 < pp )
          {
            ix = n - ix;
          }
          value = ix;
          return value;
        }

        if ( 110 < ix )
        {
          break;
        }
        u = u - f;
        ix = ix + 1;
        f = f * ( g / ( float ) ( ix ) - r );
      }
    }
  }
  ffm = xnp + p;
  m = ffm;
  fm = m;
  xnpq = xnp * q;
  p1 = ( int ) ( 2.195 * sqrt ( xnpq ) - 4.6 * q ) + 0.5;
  xm = fm + 0.5;
  xl = xm - p1;
  xr = xm + p1;
  c = 0.134 + 20.5 / ( 15.3 + fm );
  al = ( ffm - xl ) / ( ffm - xl * p );
  xll = al * ( 1.0 + 0.5 * al );
  al = ( xr - ffm ) / ( xr * q );
  xlr = al * ( 1.0 + 0.5 * al );
  p2 = p1 * ( 1.0 + c + c );
  p3 = p2 + c / xll;
  p4 = p3 + c / xlr;
//
//  Generate a variate.
//
  for ( ; ; )
  {
    u = r4_uni_01 ( ) * p4;
    v = r4_uni_01 ( );
//
//  Triangle
//
    if ( u < p1 )
    {
      ix = xm - p1 * v + u;
      if ( 0.5 < pp ) 
      {
        ix = n - ix;
      }
      value = ix;
      return value;
    }
//
//  Parallelogram
//
    if ( u <= p2 )
    {
      x = xl + ( u - p1 ) / c;
      v = v * c + 1.0 - fabs ( xm - x ) / p1;

      if ( v <= 0.0 || 1.0 < v )
      {
        continue;
      }
      ix = x;
    }
    else if ( u <= p3 )
    {
      ix = xl + log ( v ) / xll;
      if ( ix < 0 )
      {
        continue;
      }
      v = v * ( u - p2 ) * xll;
    }
    else
    {
      ix = xr - log ( v ) / xlr;
      if ( n < ix )
      {
        continue;
      }
      v = v * ( u - p3 ) * xlr;
    }
    k = abs ( ix - m );

    if ( k <= 20 || xnpq / 2.0 - 1.0 <= k )
    {
      f = 1.0;
      r = p / q;
      g = ( n + 1 ) * r;

      if ( m < ix )
      {
        mp = m + 1;
        for ( i = mp; i <= ix; i++ )
        {
          f = f * ( g / i - r );
        }
      }
      else if ( ix < m )
      {
        ix1 = ix + 1;
        for ( i = ix1; i <= m; i++ )
        {
          f = f / ( g / i - r );
        }
      }

      if ( v <= f )
      {
        if ( 0.5 < pp )
        {
          ix = n - ix;
        }
        value = ix;
        return value;
      }
    }
    else
    {
      amaxp = ( k / xnpq ) * ( ( k * ( k / 3.0 
        + 0.625 ) + 0.1666666666666 ) / xnpq + 0.5 );
      ynorm = - ( float ) ( k * k ) / ( 2.0 * xnpq );
      alv = log ( v );

      if ( alv < ynorm - amaxp )
      {
        if ( 0.5 < pp )
        {
          ix = n - ix;
        }
        value = ix;
        return value;
      }

      if ( ynorm + amaxp < alv )
      {
        continue;
      }

      x1 = ( float ) ( ix + 1 );
      f1 = fm + 1.0;
      z = ( float ) ( n + 1 ) - fm;
      w = ( float ) ( n - ix + 1 );
      z2 = z * z;
      x2 = x1 * x1;
      f2 = f1 * f1;
      w2 = w * w;

      t = xm * log ( f1 / x1 ) + ( n - m + 0.5 ) * log ( z / w ) 
        + ( float ) ( ix - m ) * log ( w * p / ( x1 * q )) 
        + ( 13860.0 - ( 462.0 - ( 132.0 - ( 99.0 - 140.0 
        / f2 ) / f2 ) / f2 ) / f2 ) / f1 / 166320.0 
        + ( 13860.0 - ( 462.0 - ( 132.0 - ( 99.0 - 140.0 
        / z2 ) / z2 ) / z2 ) / z2 ) / z / 166320.0 
        + ( 13860.0 - ( 462.0 - ( 132.0 - ( 99.0 - 140.0 
        / x2 ) / x2 ) / x2 ) / x2 ) / x1 / 166320.0 
        + ( 13860.0 - ( 462.0 - ( 132.0 - ( 99.0 - 140.0 
        / w2 ) / w2 ) / w2 ) / w2 ) / w / 166320.0;

      if ( alv <= t )
      {
        if ( 0.5 < pp )
        {
          ix = n - ix;
        }
        value = ix;
        return value;
      }
    }
  }
  return value;
}
//****************************************************************************80

int ignnbn ( int n, float p )

//****************************************************************************80
//
//  Purpose:
//
//    IGNNBN generates a negative binomial random deviate.
//
//  Discussion:
//
//    This procedure generates a single random deviate from a negative binomial
//    distribution.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Luc Devroye,
//    Non-Uniform Random Variate Generation,
//    Springer, 1986,
//    ISBN: 0387963057,
//    LC: QA274.D48.
//
//  Parameters:
//
//    Input, int N, the required number of events.
//    0 <= N.
//
//    Input, float P, the probability of an event during a 
//    Bernoulli trial.  0.0 < P < 1.0.
//
//    Output, int IGNNBN, a random deviate from 
//    the distribution.
//
 {
  float a;
  float r;
  int value;
  float y;

  if ( n < 0 )
  {
    cerr << "\n";
    cerr << "IGNNBN - Fatal error!\n";
    cerr << "  N < 0.\n";
    exit ( 1 );
  }

  if ( p <= 0.0 )
  {
    cerr << "\n";
    cerr << "IGNNBN - Fatal error!\n";
    cerr << "  P <= 0.0\n";
    exit ( 1 );
  }

  if ( 1.0 <= p )
  {
    cerr << "\n";
    cerr << "IGNNBN - Fatal error!\n";
    cerr << "  1.0 <= P\n";
    exit ( 1 );
  }
//
//  Generate Y, a random gamma (n,(1-p)/p) variable.
//
  r = ( float ) ( n );
  a = p / ( 1.0 - p );
  y = gengam ( a, r );
//
//  Generate a random Poisson ( y ) variable.
//
  value = ignpoi ( y );

  return value;
}
//****************************************************************************80

int ignpoi ( float mu )

//****************************************************************************80
//
//  Purpose:
//
//    IGNPOI generates a Poisson random deviate.
//
//  Discussion:
//
//    This procedure generates a single random deviate from a Poisson
//    distribution with given mean.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 September 2018
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Computer Generation of Poisson Deviates
//    From Modified Normal Distributions,
//    ACM Transactions on Mathematical Software,
//    Volume 8, Number 2, June 1982, pages 163-179.
//
//  Parameters:
//
//    Input, float MU, the mean of the Poisson distribution 
//    from which a random deviate is to be generated.
//
//    Output, int IGNPOI, a random deviate from
//    the distribution.
//
{
  const float a0 = -0.5;
  const float a1 =  0.3333333;
  const float a2 = -0.2500068;
  const float a3 =  0.2000118;
  const float a4 = -0.1661269;
  const float a5 =  0.1421878;
  const float a6 = -0.1384794;
  const float a7 =  0.1250060;
  float b1;
  float b2;
  float c;
  float c0;
  float c1;
  float c2;
  float c3;
  float d;
  float del;
  float difmuk;
  float e;
  const float fact[10] = { 1.0, 1.0, 2.0, 6.0, 24.0, 
    120.0, 720.0, 5040.0, 40320.0, 362880.0 };
  float fk;
  float fx;
  float fy;
  float g;
  int k;
  int kflag;
  int l;
  float omega;
  float p;
  float p0;
  float px;
  float py;
  float q;
  float s;
  float t;
  float u;
  float v;
  int value;
  float x;
  float xx;
//
//  Start new table and calculate P0.
//
  if ( mu < 10.0 )
  {
    p = exp ( - mu );
    q = p;
    p0 = p;
//
//  Uniform sample for inversion method.
//
    for ( ; ; )
    {
      u = r4_uni_01 ( );
      value = 0;

      if ( u <= p0 )
      {
        return value;
      }
//
//  Creation of new Poisson probabilities.
//
      for ( k = 1; k <= 35; k++ )
      {
        p = p * mu / ( float ) ( k );
        q = q + p;
        if ( u <= q )
        {
          value = k;
          return value;
        }
      }
    }
  }
  else
  {
    s = sqrt ( mu );
    d = 6.0 * mu * mu;
    l = ( int ) ( mu - 1.1484 );
//
//  Normal sample.
//
    g = mu + s * snorm ( );

    if ( 0.0 <= g )
    {
      value = ( int ) ( g );
//
//  Immediate acceptance if large enough.
//
      if ( l <= value )
      {
        return value;
      }
//
//  Squeeze acceptance.
//
      fk = ( float ) ( value );
      difmuk = mu - fk;
      u = r4_uni_01 ( );

      if ( difmuk * difmuk * difmuk <= d * u )
      {
        return value;
      }
    }
//
//  Preparation for steps P and Q.
//
    omega = 0.3989423 / s;
    b1 = 0.04166667 / mu;
    b2 = 0.3 * b1 * b1;
    c3 = 0.1428571 * b1 * b2;
    c2 = b2 - 15.0 * c3;
    c1 = b1 - 6.0 * b2 + 45.0 * c3;
    c0 = 1.0 - b1 + 3.0 * b2 - 15.0 * c3;
    c = 0.1069 / mu;

    if ( 0.0 <= g )
    {
      kflag = 0;

      if ( value < 10 )
      {
        px = - mu;
        py = pow ( mu, value ) / fact[value];
      }
      else
      {
        del = 0.8333333E-01 / fk;
        del = del - 4.8 * del * del * del;
        v = difmuk / fk;

        if ( 0.25 < fabs ( v ) )
        {
          px = fk * log ( 1.0 + v ) - difmuk - del;
        }
        else
        {
          px = fk * v * v * ((((((( a7 
            * v + a6 ) 
            * v + a5 ) 
            * v + a4 ) 
            * v + a3 ) 
            * v + a2 ) 
            * v + a1 ) 
            * v + a0 ) - del;
        }
        py = 0.3989423 / sqrt ( fk );
      }
      x = ( 0.5 - difmuk ) / s;
      xx = x * x;
      fx = -0.5 * xx;
      fy = omega * ((( c3 * xx + c2 ) * xx + c1 ) * xx + c0 );

      if ( fy - u * fy <= py * exp ( px - fx ) )
      {
        return value;
      }
    }
//
//  Exponential sample.
//
    for ( ; ; )
    {
      e = sexpo ( );
      u = 2.0 * r4_uni_01 ( ) - 1.0;
      if ( u < 0.0 )
      {
        t = 1.8 - fabs ( e );
      }
      else
      {
        t = 1.8 + fabs ( e );
      }

      if ( t <= -0.6744 )
      {
        continue;
      }

      value = ( int ) ( mu + s * t );
      fk = ( float ) ( value );
      difmuk = mu - fk;

      kflag = 1;
//
//  Calculation of PX, PY, FX, FY.
//
      if ( value < 10 )
      {
        px = -mu;
        py = pow ( mu, value ) / fact[value];
      }
      else
      {
        del = 0.8333333E-01 / fk;
        del = del - 4.8 * del * del * del;
        v = difmuk / fk;

        if ( 0.25 < fabs ( v ) )
        {
          px = fk * log ( 1.0 + v ) - difmuk - del;
        }
        else
        {
          px = fk * v * v * ((((((( a7 
            * v + a6 ) 
            * v + a5 ) 
            * v + a4 ) 
            * v + a3 ) 
            * v + a2 ) 
            * v + a1 ) 
            * v + a0 ) - del;
        }
        py = 0.3989423 / sqrt ( fk );
      }

      x = ( 0.5 - difmuk ) / s;
      xx = x * x;
      fx = -0.5 * xx;
      fy = omega * ((( c3 * xx + c2 ) * xx + c1 ) * xx + c0 );

      if ( kflag <= 0 )
      {
        if ( fy - u * fy <= py * exp ( px - fx ) )
        {
          return value;
        }
      }
      else
      {
        if ( c * fabs ( u ) <= py * exp ( px + e ) - fy * exp ( fx + e ) )
        {
          return value;
        }
      }
    }
  }
}
//****************************************************************************80

int ignuin ( int low, int high )

//****************************************************************************80
//
//  Purpose:
//
//    IGNUIN generates a random integer in a given range.
//
//  Discussion:
//
//    Each deviate K satisfies LOW <= K <= HIGH.
//
//    If (HIGH-LOW) > 2,147,483,561, this procedure prints an error message
//    and stops the program.
//
//    IGNLGI generates ints between 1 and 2147483562.
//
//    MAXNUM is 1 less than the maximum generatable value.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, int LOW, HIGH, the lower and upper bounds.
//
//    Output, int IGNUIN, a random deviate from 
//    the distribution.
//
{
  int ign;
  int maxnow;
  const int maxnum = 2147483561;
  int ranp1;
  int value;
  int width;

  if ( high < low )
  {
    cerr << "\n";
    cerr << "IGNUIN - Fatal error!\n";
    cerr << "  HIGH < LOW.\n";
    exit ( 1 );
  }

  width = high - low;

  if ( maxnum < width )
  {
    cerr << "\n";
    cerr << "IGNUIN - Fatal error!\n";
    cerr << "  Range HIGH-LOW is too large.\n";
    exit ( 1 );
  }

  if ( low == high )
  {
    value = low;
    return value;
  }

  ranp1 = width + 1;
  maxnow = ( maxnum / ranp1 ) * ranp1;

  for ( ; ; )
  {
    ign = i4_uni ( ) - 1;

    if ( ign <= maxnow )
    {
      break;
    }
  }

  value = low + ( ign % ranp1 );

  return value;
}
//****************************************************************************80

int lennob ( char *s )

//****************************************************************************80
//
//  Purpose:
//
//    LENNOB counts the length of a string, ignoring trailing blanks.
//
//  Discussion:
//
//    This procedure returns the length of a string up to and including
//    the last non-blank character.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, char *S, the string.
//
//    Output, int LENNOB, the length of the string to the last
//    nonblank.
//
{
  int n;
  char *t;

  n = strlen ( s );
  t = s + strlen ( s ) - 1;

  while ( 0 < n )
  {
    if ( *t != ' ' )
    {
      return n;
    }
    t--;
    n--;
  }

  return n;
}
//****************************************************************************80

void phrtsd ( char *phrase, int &seed1, int &seed2 )

//****************************************************************************80
//
//  Purpose:
//
//    PHRTST converts a phrase to a pair of random number generator seeds.
//
//  Discussion:
//
//    This procedure uses a character string to generate two seeds for the RGN
//    random number generator.
//
//    Trailing blanks are eliminated before the seeds are generated.
//
//    Generated seed values will fall in the range 1 to 2^30 = 1,073,741,824.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, char *PHRASE, a phrase to be used for the
//    random number generation.
//
//    Output, int &SEED1, &SEED2, the two seeds for the
//    random number generator, based on PHRASE.
//
{
  char c;
  char *cstar;
  int i;
  int ichr;
  int j;
  int lphr;
  int shift[5] = { 1, 64, 4096, 262144, 16777216 };
  char table[] = 
    "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&*()_+[];:'""<>?,./";
  int twop30 = 1073741824;
  int values[5];

  seed1 = 1234567890;
  seed2 = 123456789;

  lphr = lennob ( phrase );

  for ( i = 0; i < lphr; i++ )
  {
    c = phrase[i];
//
//  CSTAR points to the address of C in TABLE.
//
    cstar = strchr ( table, c );
//
//  If C does not occur, set the index to 62.
//
    if ( !cstar )
    {
      ichr = 63;
    }
//
//  Otherwise, the numerical index is the difference between the
//  addresses CSTAR and TABLE.
//
    else
    {
      ichr = cstar - table + 1;
      ichr = ichr % 64;
      if ( ichr == 0 )
      {
        ichr = 63;
      }
    }

    ichr = ichr - 1;

    for ( j = 0; j < 5; j++ )
    {
      values[j] = ichr - j;
      if ( values[j] < 1 )
      {
        values[j] = values[j] + 63;
      }
    }

    for ( j = 0; j < 5; j++ )
    {
      seed1 = ( seed1 + shift[j] * values[j]   ) % twop30;
      seed2 = ( seed2 + shift[j] * values[4-j] ) % twop30;
    }
  }
  return;
}
//****************************************************************************80

void prcomp ( int maxobs, int p, float mean[], float xcovar[], float answer[] )

//****************************************************************************80
//
//  Purpose:
//
//    PRCOMP prints covariance information.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    03 September 2018
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, int MAXOBS, the number of observations.
//
//    Input, int P, the number of variables.
//
//    Input, float MEAN[P], the mean for each column.
//
//    Input, float XCOVAR[P*P], the variance/covariance matrix.
//
//    Input, float ANSWER[MAXOBS*P], the observed values.
//
{
  float dum1;
  float dum2;
  int i;
  int j;
  float *rcovar;
  float rmean;
  float rvar;

  cout << "\n";
  cout << "PRCOMP:\n";
  cout << "  Print and compare covariance information\n";
  cout << "\n";

  for ( j = 0; j < p; j++ )
  {
    stats ( answer+j*maxobs, maxobs, rmean, rvar, dum1, dum2 );
    cout << "  Variable number " << j << "\n";
    cout << "  Mean       " << mean[j] << "  Generated " << rmean << "\n";
    cout << "  Variance   " << xcovar[j+j*p] << "  Generated " << rvar << "\n";
  }

  cout << "\n";
  cout << "  Covariances:\n";
  cout << "\n";

  rcovar = new float[p*p];

  for ( i = 0; i < p; i++ )
  {
    for ( j = 0; j < i; j++ )
    {
      cout << "  I = " << i << ", J = " << j << "\n";
      rcovar[i+j*p] = r4vec_covar ( maxobs, answer+i*p, answer+j*p );
      cout << "  Covariance " << xcovar[i+j*p] 
           << "  Generated " << rcovar[i+j*p] << "\n";
    }
  }

  delete [] rcovar;

  return;
}
//****************************************************************************80

float r4_exp ( float x )

//****************************************************************************80
//
//  Purpose:
//
//    R4_EXP computes the exponential function, avoiding overflow and underflow.
//
//  Discussion:
//
//    For arguments of very large magnitude, the evaluation of the
//    exponential function can cause computational problems.  Some languages
//    and compilers may return an infinite value or a "Not-a-Number".  
//    An alternative, when dealing with a wide range of inputs, is simply
//    to truncate the calculation for arguments whose magnitude is too large.
//    Whether this is the right or convenient approach depends on the problem
//    you are dealing with, and whether or not you really need accurate
//    results for large magnitude inputs, or you just want your code to
//    stop crashing.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2014
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, float X, the argument of the exponential function.
//
//    Output, float R4_EXP, the value of exp ( X ).
//
{
  const float r4_huge = 1.0E+30;
  const float r4_log_max = +69.0776;
  const float r4_log_min = -69.0776;
  float value;

  if ( x <= r4_log_min )
  {
    value = 0.0;
  }
  else if ( x < r4_log_max )
  {
    value = exp ( x );
  }
  else
  {
    value = r4_huge;
  }

  return value;
}
/******************************************************************************/

float r4_exponential_sample ( float lambda )

/******************************************************************************/
/*
  Purpose:

    R4_EXPONENTIAL_SAMPLE samples the exponential PDF.

  Discussion:

    Note that the parameter LAMBDA is a multiplier.  In some formulations,
    it is used as a divisor instead.

  Licensing:

    This code is distributed under the MIT license.

  Modified:

    18 April 2013

  Author:

    John Burkardt

  Parameters:

    Input, float LAMBDA, the parameter of the PDF.

    Output, float R4_EXPONENTIAL_SAMPLE, a sample of the PDF.
*/
{
  float r;
  float value;

  r = r4_uni_01 ( );

  value = - log ( r ) * lambda;

  return value;
}
//****************************************************************************80

float r4vec_covar ( int n, float x[], float y[] )

//****************************************************************************80
//
//  Purpose:
//
//    R4VEC_COVAR computes the covariance of two vectors.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    20 April 2013
//
//  Author:
//
//    John Burkardt.
//
//  Parameters:
//
//    Input, int N, the dimension of the two vectors.
//
//    Input, float X[N], Y[N], the two vectors.
//
//    Output, float R4VEC_COVAR, the covariance of the two vectors.
//
{
  int i;
  float value;
  float x_average;
  float y_average;

  x_average = 0.0;
  for ( i = 0; i < n; i++ )
  {
    x_average = x_average + x[i];
  }
  x_average = x_average / ( float ) ( n );

  y_average = 0.0;
  for ( i = 0; i < n; i++ )
  {
    y_average = y_average + x[i];
  }
  y_average = y_average / ( float ) ( n );

  value = 0.0;
  for ( i = 0; i < n; i++ )
  {
    value = value + ( x[i] - x_average ) * ( y[i] - y_average );
  }

  value = value / ( float ) ( n - 1 );

  return value;
}
/******************************************************************************/

float r8_exponential_sample ( float lambda )

/******************************************************************************/
/*
  Purpose:

    R8_EXPONENTIAL_SAMPLE samples the exponential PDF.

  Discussion:

    Note that the parameter LAMBDA is a multiplier.  In some formulations,
    it is used as a divisor instead.

  Licensing:

    This code is distributed under the MIT license.

  Modified:

    18 April 2013

  Author:

    John Burkardt

  Parameters:

    Input, double LAMBDA, the parameter of the PDF.

    Output, double R8_EXPONENTIAL_SAMPLE, a sample of the PDF.
*/
{
  double r;
  double value;

  r = r8_uni_01 ( );

  value = - log ( r ) * lambda;

  return value;
}
//****************************************************************************80

double r8vec_covar ( int n, double x[], double y[] )

//****************************************************************************80
//
//  Purpose:
//
//    R8VEC_COVAR computes the covariance of two vectors.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    20 April 2013
//
//  Author:
//
//    John Burkardt.
//
//  Parameters:
//
//    Input, int N, the dimension of the two vectors.
//
//    Input, double X[N], Y[N], the two vectors.
//
//    Output, double R8VEC_COVAR, the covariance of the two vectors.
//
{
  int i;
  double value;
  double x_average;
  double y_average;

  x_average = 0.0;
  for ( i = 0; i < n; i++ )
  {
    x_average = x_average + x[i];
  }
  x_average = x_average / ( double ) ( n );

  y_average = 0.0;
  for ( i = 0; i < n; i++ )
  {
    y_average = y_average + x[i];
  }
  y_average = y_average / ( double ) ( n );

  value = 0.0;
  for ( i = 0; i < n; i++ )
  {
    value = value + ( x[i] - x_average ) * ( y[i] - y_average );
  }

  value = value / ( double ) ( n - 1 );

  return value;
}
//****************************************************************************80

int s_eqi ( char *s1, char *s2 )

//****************************************************************************80
//
//  Purpose:
//
//    S_EQI reports whether two strings are equal, ignoring case.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    03 June 2008
//
//  Author:
//
//    John Burkardt
//
//  Parameters:
//
//    Input, char *S1, char *S2, pointers to two strings.
//
//    Output, int S_EQI, is true if the strings are equal.
//
{
  int i;
  int nchar;
  int nchar1;
  int nchar2;

  nchar1 = strlen ( s1 );
  nchar2 = strlen ( s2 );
  if ( nchar1 < nchar2 )
  {
    nchar = nchar1;
  }
  else
  {
    nchar = nchar2;
  }
//
//  The strings are not equal if they differ over their common length.
//
  for ( i = 0; i < nchar; i++ )
  {

    if ( ch_cap ( s1[i] ) != ch_cap ( s2[i] ) )
    {
      return 0;
    }
  }
//
//  The strings are not equal if the longer one includes nonblanks
//  in the tail.
//
  if ( nchar < nchar1 )
  {
    for ( i = nchar; i < nchar1; i++ )
    {
      if ( s1[i] != ' ' )
      {
        return 0;
      }
    }
  }
  else if ( nchar < nchar2 )
  {
    for ( i = nchar; i < nchar2; i++ )
    {
      if ( s2[i] != ' ' )
      {
        return 0;
      }
    }
  }

  return 1;
}
//****************************************************************************80

float sdot ( int n, float dx[], int incx, float dy[], int incy )

//****************************************************************************80
//
//  Purpose:
//
//    SDOT forms the dot product of two vectors.
//
//  Discussion:
//
//    This routine uses unrolled loops for increments equal to one.
//
//  Licensing:
//
//    This code is distributed under the MIT license. 
//
//  Modified:
//
//    29 March 2007
//
//  Author:
//
//    C++ version by John Burkardt
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch, Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, 1979,
//    ISBN13: 978-0-898711-72-1,
//    LC: QA214.L56.
//
//    Charles Lawson, Richard Hanson, David Kincaid, Fred Krogh,
//    Algorithm 539: 
//    Basic Linear Algebra Subprograms for Fortran Usage,
//    ACM Transactions on Mathematical Software, 
//    Volume 5, Number 3, September 1979, pages 308-323.
//
//  Parameters:
//
//    Input, int N, the number of entries in the vectors.
//
//    Input, float DX[*], the first vector.
//
//    Input, int INCX, the increment between successive entries in DX.
//
//    Input, float DY[*], the second vector.
//
//    Input, int INCY, the increment between successive entries in DY.
//
//    Output, float SDOT, the sum of the product of the corresponding
//    entries of DX and DY.
//
{
  float dtemp;
  int i;
  int ix;
  int iy;
  int m;

  dtemp = 0.0;

  if ( n <= 0 )
  {
    return dtemp;
  }
//
//  Code for unequal increments or equal increments
//  not equal to 1.
//
  if ( incx != 1 || incy != 1 )
  {
    if ( 0 <= incx )
    {
      ix = 0;
    }
    else
    {
      ix = ( - n + 1 ) * incx;
    }

    if ( 0 <= incy )
    {
      iy = 0;
    }
    else
    {
      iy = ( - n + 1 ) * incy;
    }

    for ( i = 0; i < n; i++ )
    {
      dtemp = dtemp + dx[ix] * dy[iy];
      ix = ix + incx;
      iy = iy + incy;
    }
  }
//
//  Code for both increments equal to 1.
//
  else
  {
    m = n % 5;

    for ( i = 0; i < m; i++ )
    {
      dtemp = dtemp + dx[i] * dy[i];
    }

    for ( i = m; i < n; i = i + 5 )
    {
      dtemp = dtemp + dx[i  ] * dy[i  ] 
                    + dx[i+1] * dy[i+1] 
                    + dx[i+2] * dy[i+2] 
                    + dx[i+3] * dy[i+3] 
                    + dx[i+4] * dy[i+4];
    }

  }

  return dtemp;
}
//****************************************************************************80

float *setcov ( int p, float var[], float corr )

//****************************************************************************80
//
//  Purpose:
//
//    SETCOV sets a covariance matrix from variance and common correlation.
//
//  Discussion:
//
//    This procedure sets the covariance matrix from the variance and
//    common correlation.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, int P, the number of variables.
//
//    Input, float VAR[P], the variances.
//
//    Input, float CORR, the common correlaton.
//
//    Output, float COVAR[P*P], the covariance matrix.
//
{
  float *covar;
  int i;
  int j;

  covar = new float[p*p];

  for ( j = 0; j < p; j++ )
  {
    for ( i = 0; i < p; i++ )
    {
      if ( i == j )
      {
        covar[i+j*p] = var[i];
      }
      else
      {
        covar[i+j*p] = corr * sqrt ( var[i] * var[j] );
      }
    }
  }

  return covar;
}
//****************************************************************************80

void setgmn ( float meanv[], float covm[], int p, float parm[] )

//****************************************************************************80
//
//  Purpose:
//
//    SETGMN sets data for the generation of multivariate normal deviates.
//
//  Discussion:
//
//    This procedure places P, MEANV, and the Cholesky factorization of
//    COVM in GENMN.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float MEANV[P], the means of the multivariate 
//    normal distribution.
//
//    Input/output, float COVM[P*P].  On input, the covariance
//    matrix of the multivariate distribution.  On output, the information 
//    in COVM has been overwritten.
//
//    Input, int P, the number of dimensions.
//
//    Output, float PARM[P*(P+3)/2+1], parameters needed to generate
//    multivariate normal deviates.
//
{
  int i;
  int icount;
  int info;
  int j;

  if ( p <= 0 )
  {
    cerr << "\n";
    cerr << "SETGMN - Fatal error!\n";
    cerr << "  P was not positive.\n";
    exit ( 1 );
  }
//
//  Store P.
//
  parm[0] = p;
//
//  Store MEANV.
//
  for ( i = 0; i < p; i++ )
  {
    parm[1+i] = meanv[i];
  }
//
//  Compute the Cholesky decomposition.
//
  info = spofa ( covm, p, p );

  if ( info != 0 )
  {
    cerr << "\n";
    cerr << "SETGMN - Fatal error!\n";
    cerr << "  SPOFA finds COVM not positive definite.\n";
    exit ( 1 );
  }
//
//  Store the upper half of the Cholesky factor.
//
  icount = p + 1;

  for ( i = 0; i < p; i++ )
  {
    for ( j = i; j < p; j++ )
    {
      parm[icount] = covm[i+j*p];
      icount = icount + 1;
    }
  }
  return;
}
//****************************************************************************80

float sexpo ( )

//****************************************************************************80
//
//  Purpose:
//
//    SEXPO samples the standard exponential distribution.
//
//  Discussion:
//
//    This procedure corresponds to algorithm SA in the reference.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Computer Methods for Sampling From the
//    Exponential and Normal Distributions,
//    Communications of the ACM,
//    Volume 15, Number 10, October 1972, pages 873-882.
//
//  Parameters:
//
//    Output, float SEXPO, a random deviate from the standard
//    exponential distribution.
//
{
  float a;
  int i;
  const float q[8] = {
       0.6931472, 
       0.9333737, 
       0.9888778, 
       0.9984959, 
       0.9998293, 
       0.9999833, 
       0.9999986, 
       0.9999999 };
  float u;
  float umin;
  float ustar;
  float value;

  a = 0.0;
  u = r4_uni_01 ( );

  for ( ; ; )
  {
    u = u + u;

    if ( 1.0 < u )
    {
      break;
    }
    a = a + q[0];
  }

  u = u - 1.0;

  if ( u <= q[0] )
  {
    value = a + u;
    return value;
  }

  i = 0;
  ustar = r4_uni_01 ( );
  umin = ustar;

  for ( ; ; )
  {
    ustar = r4_uni_01 ( );
    umin = fmin ( umin, ustar );
    i = i + 1;

    if ( u <= q[i] )
    {
      break;
    }
  }

  value = a + umin * q[0];

  return value;
}
//****************************************************************************80

float sgamma ( float a )

//****************************************************************************80
//
//  Purpose:
//
//    SGAMMA samples the standard Gamma distribution.
//
//  Discussion:
//
//    This procedure corresponds to algorithm GD in the reference.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Generating Gamma Variates by a Modified Rejection Technique,
//    Communications of the ACM,
//    Volume 25, Number 1, January 1982, pages 47-54.
//
//  Parameters:
//
//    Input, float A, the parameter of the standard gamma
//    distribution.  0.0 < A < 1.0.
//
//    Output, float SGAMMA, a random deviate from the distribution.
//
{
  const float a1 =  0.3333333;
  const float a2 = -0.2500030;
  const float a3 =  0.2000062;
  const float a4 = -0.1662921;
  const float a5 =  0.1423657;
  const float a6 = -0.1367177;
  const float a7 =  0.1233795;
  float b;
  float c;
  float d;
  float e;
  const float e1 = 1.0;
  const float e2 = 0.4999897;
  const float e3 = 0.1668290;
  const float e4 = 0.0407753;
  const float e5 = 0.0102930;
  float p;
  float q;
  float q0;
  const float q1 =  0.04166669;
  const float q2 =  0.02083148;
  const float q3 =  0.00801191;
  const float q4 =  0.00144121;
  const float q5 = -0.00007388;
  const float q6 =  0.00024511;
  const float q7 =  0.00024240;
  float r;
  float s;
  float s2;
  float si;
  const float sqrt32 = 5.656854;
  float t;
  float u;
  float v;
  float value;
  float w;
  float x;

  if ( 1.0 <= a )
  {
    s2 = a - 0.5;
    s = sqrt ( s2 );
    d = sqrt32 - 12.0 * s;
//
//  Immediate acceptance.
//
    t = snorm ( );
    x = s + 0.5 * t;
    value = x * x;

    if ( 0.0 <= t )
    {
      return value;
    }
//
//  Squeeze acceptance.
//
    u = r4_uni_01 ( );
    if ( d * u <= t * t * t )
    {
      return value;
    }

    r = 1.0 / a;
    q0 = (((((( q7 
      * r + q6 ) 
      * r + q5 ) 
      * r + q4 ) 
      * r + q3 ) 
      * r + q2 ) 
      * r + q1 ) 
      * r;
//
//  Approximation depending on size of parameter A.
//
    if ( 13.022 < a )
    {
      b = 1.77;
      si = 0.75;
      c = 0.1515 / s;
    }
    else if ( 3.686 < a )
    {
      b = 1.654 + 0.0076 * s2;
      si = 1.68 / s + 0.275;
      c = 0.062 / s + 0.024;
    }
    else
    {
      b = 0.463 + s + 0.178 * s2;
      si = 1.235;
      c = 0.195 / s - 0.079 + 0.16 * s;
    }
//
//  Quotient test.
//
    if ( 0.0 < x )
    {
      v = 0.5 * t / s;

      if ( 0.25 < fabs ( v ) )
      {
        q = q0 - s * t + 0.25 * t * t + 2.0 * s2 * log ( 1.0 + v );
      }
      else
      {
        q = q0 + 0.5 * t * t * (((((( 
            a7   * v 
          + a6 ) * v 
          + a5 ) * v 
          + a4 ) * v 
          + a3 ) * v 
          + a2 ) * v 
          + a1 ) * v;
      }

      if ( log ( 1.0 - u ) <= q )
      {
        return value;
      }
    }

    for ( ; ; )
    {
      e = sexpo ( );
      u = 2.0 * r4_uni_01 ( ) - 1.0;
 
      if ( 0.0 <= u )
      {
        t = b + fabs ( si * e );
      }
      else
      {
        t = b - fabs ( si * e );
      }
//
//  Possible rejection.
//
      if ( t < -0.7187449 )
      {
        continue;
      }
//
//  Calculate V and quotient Q.
//
      v = 0.5 * t / s;

      if ( 0.25 < fabs ( v ) )
      {
        q = q0 - s * t + 0.25 * t * t + 2.0 * s2 * log ( 1.0 + v );
      }
      else
      {
        q = q0 + 0.5 * t * t * (((((( 
            a7   * v 
          + a6 ) * v 
          + a5 ) * v 
          + a4 ) * v 
          + a3 ) * v 
          + a2 ) * v 
          + a1 ) * v;
      }
//
//  Hat acceptance.
//
      if ( q <= 0.0 )
      {
        continue;
      }

      if ( 0.5 < q )
      {
        w = exp ( q ) - 1.0;
      }
      else
      {
        w = (((( e5 * q + e4 ) * q + e3 ) * q + e2 ) * q + e1 ) * q;
      }
//
//  May have to sample again.
//
      if ( c * fabs ( u ) <= w * exp ( e - 0.5 * t * t ) )
      {
        break;
      }
    }

    x = s + 0.5 * t;
    value = x * x;
  }
//
//  Method for A < 1.
//
  else if ( a < 1.0 )
  {
    b = 1.0 + 0.3678794 * a;

    for ( ; ; )
    {
      p = b * r4_uni_01 ( );

      if ( p < 1.0 )
      {
        value = exp ( log ( p ) / a );
        if ( value <= sexpo ( ) )
        {
          break;
        }
      }
      else
      {
        value = - log ( ( b - p ) / a );
        if ( ( 1.0 - a ) * log ( value ) <= sexpo ( ) )
        {
          break;
        }
      }
    }
  }
  return value;
}
//****************************************************************************80

float snorm ( )

//****************************************************************************80
//
//  Purpose:
//
//    SNORM samples the standard normal distribution.
//
//  Discussion:
//
//    This procedure corresponds to algorithm FL, with M = 5, in the reference.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    01 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Joachim Ahrens, Ulrich Dieter,
//    Extensions of Forsythe's Method for Random
//    Sampling from the Normal Distribution,
//    Mathematics of Computation,
//    Volume 27, Number 124, October 1973, page 927-937.
//
//  Parameters:
//
//    Output, float SNORM, a random deviate from the distribution.
//
{
  const float a[32] = {
        0.0000000, 0.3917609E-01, 0.7841241E-01, 0.1177699, 
        0.1573107, 0.1970991,     0.2372021,     0.2776904, 
        0.3186394, 0.3601299,     0.4022501,     0.4450965, 
        0.4887764, 0.5334097,     0.5791322,     0.6260990, 
        0.6744898, 0.7245144,     0.7764218,     0.8305109, 
        0.8871466, 0.9467818,     1.009990,      1.077516, 
        1.150349,  1.229859,      1.318011,      1.417797, 
        1.534121,  1.675940,      1.862732,      2.153875 };
  float aa;
  const float d[31] = {
        0.0000000, 0.0000000, 0.0000000, 0.0000000, 
        0.0000000, 0.2636843, 0.2425085, 0.2255674, 
        0.2116342, 0.1999243, 0.1899108, 0.1812252, 
        0.1736014, 0.1668419, 0.1607967, 0.1553497, 
        0.1504094, 0.1459026, 0.1417700, 0.1379632, 
        0.1344418, 0.1311722, 0.1281260, 0.1252791, 
        0.1226109, 0.1201036, 0.1177417, 0.1155119, 
        0.1134023, 0.1114027, 0.1095039 };
  const float h[31] = {
        0.3920617E-01, 0.3932705E-01, 0.3950999E-01, 0.3975703E-01, 
        0.4007093E-01, 0.4045533E-01, 0.4091481E-01, 0.4145507E-01, 
        0.4208311E-01, 0.4280748E-01, 0.4363863E-01, 0.4458932E-01, 
        0.4567523E-01, 0.4691571E-01, 0.4833487E-01, 0.4996298E-01, 
        0.5183859E-01, 0.5401138E-01, 0.5654656E-01, 0.5953130E-01, 
        0.6308489E-01, 0.6737503E-01, 0.7264544E-01, 0.7926471E-01, 
        0.8781922E-01, 0.9930398E-01, 0.1155599,     0.1404344, 
        0.1836142,     0.2790016,     0.7010474 };
  int i;
  float s;
  const float t[31] = {
        0.7673828E-03, 0.2306870E-02, 0.3860618E-02, 0.5438454E-02, 
        0.7050699E-02, 0.8708396E-02, 0.1042357E-01, 0.1220953E-01, 
        0.1408125E-01, 0.1605579E-01, 0.1815290E-01, 0.2039573E-01, 
        0.2281177E-01, 0.2543407E-01, 0.2830296E-01, 0.3146822E-01, 
        0.3499233E-01, 0.3895483E-01, 0.4345878E-01, 0.4864035E-01, 
        0.5468334E-01, 0.6184222E-01, 0.7047983E-01, 0.8113195E-01, 
        0.9462444E-01, 0.1123001,     0.1364980,     0.1716886, 
        0.2276241,     0.3304980,     0.5847031 };
  float tt;
  float u;
  float ustar;
  float value;
  float w;
  float y;

  u = r4_uni_01 ( );
  if ( u <= 0.5 )
  {
    s = 0.0;
  }
  else
  {
    s = 1.0;
  }
  u = 2.0 * u - s;
  u = 32.0 * u;
  i = ( int ) ( u );
  if ( i == 32 )
  {
    i = 31;
  }
//
//  Center
//
  if ( i != 0 )
  {
    ustar = u - ( float ) ( i );
    aa = a[i-1];

    for ( ; ; )
    {
      if ( t[i-1] < ustar )
      {
        w = ( ustar - t[i-1] ) * h[i-1];

        y = aa + w;

        if ( s != 1.0 )
        {
          value = y;
        }
        else
        {
          value = -y;
        }
        return value;
      }
      u = r4_uni_01 ( );
      w = u * ( a[i] - aa );
      tt = ( 0.5 * w + aa ) * w;

      for ( ; ; )
      {
        if ( tt < ustar )
        {
          y = aa + w;
          if ( s != 1.0 )
          {
            value = y;
          }
          else
          {
            value = -y;
          }
          return value;
        }

        u = r4_uni_01 ( );

        if ( ustar < u )
        {
          break;
        }
        tt = u;
        ustar = r4_uni_01 ( );
      }
      ustar = r4_uni_01 ( );
    }
  }
//
//  Tail
//
  else
  {
    i = 6;
    aa = a[31];

    for ( ; ; )
    {
      u = u + u;

      if ( 1.0 <= u )
      {
        break;
      }
      aa = aa + d[i-1];
      i = i + 1;
    }

    u = u - 1.0;
    w = u * d[i-1];
    tt = ( 0.5 * w + aa ) * w;

    for ( ; ; )
    {
      ustar = r4_uni_01 ( );

      if ( tt < ustar )
      {
        y = aa + w;
        if ( s != 1.0 )
        {
          value = y;
        }
        else
        {
          value = -y;
        }
        return value;
      }

      u = r4_uni_01 ( );

      if ( u <= ustar )
      {
        tt = u;
      }
      else
      {
        u = r4_uni_01 ( );
        w = u * d[i-1];
        tt = ( 0.5 * w + aa ) * w;
      }
    }
  }
}
//****************************************************************************80

int spofa ( float a[], int lda, int n )

//****************************************************************************80
//
//  Purpose:
//
//    SPOFA factors a real symmetric positive definite matrix.
//
//  Discussion:
//
//    SPOFA is usually called by SPOCO, but it can be called
//    directly with a saving in time if RCOND is not needed.
//
//  Licensing:
//
//    This code is distributed under the MIT license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the symmetric matrix
//    to be  factored.  Only the diagonal and upper triangle are used.
//    On output, an upper triangular matrix R so that A = R'*R
//    where R' is the transpose.  The strict lower triangle is unaltered.
//    If INFO /= 0, the factorization is not complete.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Output, int SPOFA, error flag.
//    0, for normal return.
//    K, signals an error condition.  The leading minor of order K is not
//    positive definite.
//
{
  int info;
  int j;
  int k;
  float s;
  float t;

  for ( j = 1; j <= n; j++ )
  {
    s = 0.0;

    for ( k = 1; k <= j-1; k++ )
    {
      t = a[k-1+(j-1)*lda] - sdot ( k-1, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
      t = t / a[k-1+(k-1)*lda];
      a[k-1+(j-1)*lda] = t;
      s = s + t * t;
    }

    s = a[j-1+(j-1)*lda] - s;

    if ( s <= 0.0 )
    {
      info = j;
      return info;
    }

    a[j-1+(j-1)*lda] = sqrt ( s );
  }

  info = 0;

  return info;
}
//****************************************************************************80

void stats ( float x[], int n, float &av, float &var, float &xmin, float &xmax )

//****************************************************************************80
//
//  Purpose:
//
//    STATS computes statistics for a given array.
//
//  Discussion:
//
//    This procedure computes the average and variance of an array.
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    02 April 2013
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, float X[N], the array to be analyzed.
//
//    Input, int N, the dimension of the array.
//
//    Output, float &AV, the average value.
//
//    Output, float &VAR, the variance.
//
//    Output, float &XMIN, &XMAX, the minimum and maximum entries.
//
{
  int i;
  float total;

  xmin = x[0];
  xmax = x[0];
  total = x[0];
  for ( i = 1; i < n; i++ )
  {
    total = total + x[i];
    xmin = fmin ( xmin, x[i] );
    xmax = fmax ( xmax, x[i] );
  }

  av = total / ( float ) ( n );

  total = 0.0;
  for ( i = 0; i < n; i++ )
  {
    total = total + pow ( x[i] - av, 2 );
  }
  var = total / ( float ) ( n - 1 );

  return;
}
//****************************************************************************80

void trstat ( string pdf, float parin[], float &av, float &var )

//****************************************************************************80
//
//  Purpose:
//
//    TRSTAT returns the mean and variance for distributions.
//
//  Discussion:
//
//    Although I really want to do a case-insensitive comparison, for some
//    reason STRCMPI is NOT available to C, while STRCMP is.
//
//    This procedure returns the mean and variance for a number of statistical
//    distributions as a function of their parameters.
//
//    The input vector PARIN is used to pass in the parameters necessary
//    to specify the distribution.  The number of these parameters varies
//    per distribution, and it is necessary to specify an ordering for the
//    parameters used to a given distribution.  The ordering chosen here
//    is as follows:
//
//    bet
//      PARIN(1) is A
//      PARIN(2) is B
//    bin
//      PARIN(1) is Number of trials
//      PARIN(2) is Prob Event at Each Trial
//    chi
//      PARIN(1) = df
//    exp
//      PARIN(1) = mu
//    f
//      PARIN(1) is df numerator
//      PARIN(2) is df denominator
//    gam
//      PARIN(1) is A
//      PARIN(2) is R
//    nbn
//      PARIN(1) is N
//      PARIN(2) is P
//    nch
//      PARIN(1) is df
//      PARIN(2) is noncentrality parameter
//    nf
//      PARIN(1) is df numerator
//      PARIN(2) is df denominator
//      PARIN(3) is noncentrality parameter
//    nor
//      PARIN(1) is mean
//      PARIN(2) is standard deviation
//    poi
//      PARIN(1) is Mean
//    unf
//      PARIN(1) is LOW bound
//      PARIN(2) is HIGH bound
//
//  Licensing:
//
//    This code is distributed under the MIT license.
//
//  Modified:
//
//    19 September 2014
//
//  Author:
//
//    Original FORTRAN77 version by Barry Brown, James Lovato.
//    C++ version by John Burkardt.
//
//  Parameters:
//
//    Input, string PDF, indicates the distribution:
//    'bet'  beta distribution
//    'bin'  binomial
//    'chi'  chisquare
//    'exp'  exponential
//    'f'    F (variance ratio)
//    'gam'  gamma
//    'nbn'  negative binomial
//    'nch'  noncentral chisquare
//    'nf'   noncentral f
//    'nor'  normal
//    'poi'  Poisson
//    'unf'  uniform
//
//    Input, float PARIN[*], the parameters of the distribution.
//
//    Output, float &AV, the mean of the specified distribution.
//
//    Output, float &VAR, the variance of the specified distribuion.
//
{
  float a;
  float b;
  int n;
  float p;
  float r;
  float width;

  if ( pdf == "bet" )
  {
    av = parin[0] / ( parin[0] + parin[1] );
    var = ( av * parin[1] ) / ( ( parin[0] + parin[1] ) * 
      ( parin[0] + parin[1] + 1.0 ) );
  }
  else if ( pdf == "bin" )
  {
    n = ( int ) ( parin[0] );
    p = parin[1];
    av = ( float ) ( n ) * p;
    var = ( float ) ( n ) * p * ( 1.0 - p );
  }
  else if ( pdf == "chi" )
  {
    av = parin[0];
    var = 2.0 * parin[0];
  }
  else if ( pdf == "exp" )
  {
    av = parin[0];
    var = pow ( parin[0], 2 );
  }
  else if ( pdf == "f" )
  {
    if ( parin[1] <= 2.0001 )
    {
      av = -1.0;
    }
    else
    {
      av = parin[1] / ( parin[1] - 2.0 );
    }

    if ( parin[1] <= 4.0001 )
    {
      var = -1.0;
    }
    else
    {
      var = ( 2.0 * pow ( parin[1], 2 ) * ( parin[0] + parin[1] - 2.0 ) ) / 
        ( parin[0] * pow ( parin[1] - 2.0, 2 ) * ( parin[1] - 4.0 ) );
    }
  }
  else if ( pdf == "gam" )
  {
    a = parin[0];
    r = parin[1];
    av = r / a;
    var = r / a / a;
  }
  else if ( pdf == "nbn" )
  {
    n = ( int ) ( parin[0] );
    p = parin[1];
    av = n * ( 1.0 - p ) / p;
    var = n * ( 1.0 - p ) / p / p;
  }
  else if ( pdf == "nch" )
  {
    a = parin[0] + parin[1];
    b = parin[1] / a;
    av = a;
    var = 2.0 * a * ( 1.0 + b );
  }
  else if ( pdf == "nf" )
  {
    if ( parin[1] <= 2.0001 )
    {
      av = -1.0;
    }
    else
    {
      av = ( parin[1] * ( parin[0] + parin[2] ) ) 
        / ( ( parin[1] - 2.0 ) * parin[0] );
    }

    if ( parin[1] <= 4.0001 )
    {
      var = -1.0;
    }
    else
    {
      a = pow ( parin[0] + parin[2], 2 )
        + ( parin[0] + 2.0 * parin[2] ) * ( parin[1] - 2.0 );
      b = pow ( parin[1] - 2.0, 2 ) * ( parin[1] - 4.0 );
      var = 2.0 * pow ( parin[1] / parin[0], 2 ) * ( a / b );
    }
  }
  else if ( pdf == "nor" )
  {
    av = parin[0];
    var = pow ( parin[1], 2 );
  }
  else if ( pdf == "poi" )
  {
    av = parin[0];
    var = parin[0];
  }
  else if ( pdf == "unf" )
  {
    width = parin[1] - parin[0];
    av = parin[0] + width / 2.0;
    var = width * width / 12.0;
  }
  else
  {
    cerr << "\n";
    cerr << "TRSTAT - Fatal error!\n";
    cerr << "  Illegal input value for PDF.\n";
    cerr << "  PDF = \"" << pdf << "\"\n";
    exit ( 1 );
  }
  return;
}