STK_PARAM_GLS computes a generalised least squares estimate

 CALL: BETA = stk_param_gls (MODEL, XI, ZI)

   computes the generalised least squares estimate BETA of the vector of
   coefficients for the linear part of MODEL, where XI and ZI stand for
   the evaluation points and observed responses, respectively.

 CALL: [BETA, SIGMA2] = stk_param_gls (MODEL, XI, ZI)

   also returns the associated unbiased estimate SIGMA2 of sigma^2, assu-
   ming that the actual covariance matrix of the Gaussian process part of
   the model is sigma^2 K, with K the covariance matrix built from MODEL.

   SIGMA2 is actually the "best" unbiased estimate of sigma^2 :

                 1
      SIGMA2 = ----- * || ZI - P BETA ||^2_{K^{-1}}
               n - r

   where n is the number of observations, r the length of BETA, P the
   design matrix for the linear part of the model, and || . ||_{K^{-1}}
   the norm associated to the positive definite matrix K^{-1}. It is the
   best estimate with respect to the quadratic risk, among all unbiased
   estimates which are quadratic in the residuals.