#include <math.h>
extern double par[250], pri;
extern int npar;
void repar(double *theta, int *nk)
/*
  This routine is used to reparameterize the model, if desired.
  (Reparameterization is often desirable to make the form of the posterior
  closer to a multivariate normal.  See documentation in directory above.)
  The code in this file is used for the univariate regression model;
  editing is required for new models.
  If parameters are not transformed, it is still necessary to copy the
  appropriate information from par into theta and return nk.
  Inputs (external)
  pri       Logarithm of prior distribution at current parameter vector
  par       Current record read from posterior simulation file;
            parameter values are typically contained within this record.
  npar      Number of elements in par
  Outputs:
  pri       Logarithm of prior distribution at transformed current parameter
            vector
  theta     Transformed parameter vector
  nk        Number of elements in transformed parameter vector                
*/
{
   int i=1;
   *nk=npar/2;
   dcopy_(nk,par,&i,theta,&i);
   *nk=*nk+1;
   theta[*nk-1]=log(par[npar-1]);
   pri = pri + theta[*nk-1];
}   

void repar0()
/*
  This procedure is typically used to read from file information that
  may be required to interpret par in repar().  repar0() is entered once,
  before repar().  
*/
{
}

int lrange(int nk, double *theta, int *lall)
/*
  This function indicates whether or not a parameter vector (as
  reparameterized in repar) is in the support of the prior
  Inputs:
  nk         Number of parameters in the vector
  theta      Parameter vector
  Outputs:
  lall       Set 1 if theta in Euclidean nk-space is in the
             support of the prior; otherwise set 0
  lrange     Set 1 if theta is in the support of the prior, else 0 
*/
{
  *lall=1;
  return 1;
}