Unscaled features in `tstab.gpd` lead to failure of optimization routines

[lbelzile]

Reported by John Ery.

Error in t(c(1, -thresh[i] + thresh[1])) %*% gpdu$vcov : 
  requires numeric/complex matrix/vector arguments
In addition: Warning message:
In gp.fit(xdat = na.omit(as.vector(xdat)), threshold = threshold,  :
  Cannot calculate standard error based on observed information

This error is caused by unscaled features (approximately 10e9); the numerical tolerance is too small, leading to lack of convergence in the optimizer and warning/failure of the routine.

Perhaps it would make sense to scale data first before computing and using location-scale properties to give back the estimates.

[lbelzile]

It is easy to scale threshold exceedances / do a location/scale normalization of the maxima, but must then back-transform the output before computing standard errors, log-likelihood, etc.

This would require some checks to make sure it indeed improves the optimization in fit.gpd, etc.

The problem is reported in a particular function that uses it's own routines (it's now possible to fix parameters with the latest versions), but none of the profiling functions does this).