fit_gpr_sim_model: Fit A Gaussian Process Regression Function
In bdpopt: Optimisation of Bayesian Decision Problems

fit.gpr method for objects of class sim.model.

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## S3 method for class 'sim.model'
fit.gpr(model, start, gr = TRUE, method = "L-BFGS-B",
lower = 0, upper = Inf, control = list())

`model`	A model object obtained as the return value from `eval.on.grid`.
`start`	Start value passed on to `optim` when performing the marginal likelihood optimisation to find appropriate values for the hyperparameters for the GPR regression function.
`gr`	Set to `TRUE` if gradient information should be passed to `optim`. If false, `optim` uses a finite difference approximation of the gradient when performing the optimisation of the hyperparameters.
`method`	The optimisation method to be used by `optim`. One of `"Nelder-Mead"`, `"BFGS",` `"CG"`, `"L-BFGS-B"`, `"SANN"` or `"Brent"`.
`lower`	A numeric, atomic vector containing the lower limits for the hyperparameters. The first entry is for the standard deviation parameter and the remaining entries are for the length parameters. If supplied, all elements must be >= 0.
`upper`	A numeric, atomic vector containing the upper limits for the hyperparameters. The first entry is for the standard deviation parameter and the remaining entries are for the length parameters.
`control`	A list of control parameters passed on to `optim`.