STK_PARAM_ESTIM estimates the parameters of a covariance function

 CALL: PARAM = stk_param_estim (MODEL, XI, YI, PARAM0)
 CALL: [PARAM, LNV] = stk_param_estim (MODEL, XI, YI, PARAM0)

   estimates the parameters PARAM of the covariance function in MODEL
   from the data (XI, YI) using the restricted maximum likelihood (ReML)
   method.  The value PARAM0 is used as a starting point for local
   optimization.

   The observations are assumed to be noisy if MODEL.lognoisevariance is
   not -inf. In this case, the variance of the noise is estimated if
   MODEL.lognoisevariance is nan, and assumed known otherwise. The
   estimated log-variance is returned as the second output argument LNV
   (equal to MODEL.lognoisevariance when it is assumed to be known).

 CALL: PARAM = stk_param_estim (MODEL, XI, YI)
 CALL: [PARAM, LNV] = stk_param_estim (MODEL, XI, YI)

   does the same thing but uses stk_param_init to provide a starting value
   automatically.

 CALL: [PARAM, LNV] = stk_param_estim (MODEL, XI, YI, PARAM0, LNV0)

   additionally provides an initial guess LNV0 for the logarithm of the
   noise variance. In this case the observations are automatically assumed
   to be noisy, and the value of MODEL.lognoisevariance is ignored.

 CALL: PARAM = stk_param_estim (MODEL, XI, YI, PARAM0, [], CRIT)
 CALL: [PARAM, LNV] = stk_param_estim (MODEL, XI, YI, PARAM0, LNV0, CRIT)

   uses the estimation criterion CRIT instead of the default ReML criterion.

 EXAMPLES: see, e.g., stk_example_kb02, stk_example_kb03, stk_example_kb04,
           stk_example_kb06, stk_example_misc02