 Usage:  {b,bv,it,ret} = glmcore(code,x,y,wx,off,ctrl)  
 
 Input:

  code                   text string, the short code for the model (e.g. 
                         "bilo" for logit or "noid" for ordinary LS). 
                         
  x                      n x p matrix, the predictor variables. 
                         
  y                      n x 1 or n x 2 or n x 3 matrix, 
                         either the response values, 
                         or sums of response values in 1st column and sums of 
                         a function of response values in the 2nd column (e.g. 
                         sums of y^2 or log(y)). 
                         (In the case of replicated data, the number of 
                         replications should be given in wx, y[,1] contains 
                         the sums of all responses for a replication, 
                         y[,2] contains sums of y^2 or log(y) for a 
                         replication.) 
                         
  wx                     n x 1 vector or scalar, weights. Set wx=1 to ignore. 
                         
  off                    n x 1 vector or scalar, offset. Set off=0 to ignore. 
                         
  ctrl                   6 x 1 vector or scalar, contains control parameters 
                         shf (default=0), 
                         miter (default=10), 
                         cnv (default=0.0001), 
                         fscor (default=0), 
                         pow (default=0, power for power link), 
                         nbk (default=1, parameter for negative binomial). 
                         Alternatively, one can give here shf only. Set to 0 
                         to use the defaults. 
                         The parameters correspond to the optional parameters 
                         which can be given in glminit. 
                         They are all ignored when not applicable. 
                         
 Output:

  b                      p x 1 vector, estimated coefficients. 
                         
  bv                     p x p matrix, estimated covariance matrix for b. 
                         Not yet corrected for dispersion! 
                         
  it                     integer, number of iterations needed. 
                         
  ret                    scalar, return code: 
                         0 o.k., 
                         1 maximal number of iterations reached 
                         (if applicable), 
                         -1 missing values have been encountered. 
                         
--------------------------------------------------------------
(C) MD*TECH Method and Data Technologies, 17.8.2000
