Vector for gradient of likelihood w.r.t. x (theta)
Arguments
theta
Log of correlation parameters
CGGP
CGGP object
y
CGGP$design measured values
...
Forces you to name remaining arguments
return_lik
If yes, it returns a list with lik and glik
ys
Supplementary output data
Xs
Supplementary input data
HandlingSuppData
How should supplementary data be handled?
* Correct: full likelihood with grid and supplemental data
* Only: only use supplemental data
* Ignore: ignore supplemental data