a suitable distance function of type f(x1,x2), returning a scalar distance value, preferably between 0 and 1. Maximum distances larger 1 are no problem, but may yield scaling bias when different measures are compared. Should be non-negative and symmetric. In case Kriging is chosen, it can also be a list of several distance functions. In this case, MLE is used to determine the most suited distance measure (see the last reference).

list with options:

model

Model to be used as a surrogate of the target function. Default is "K" (Kriging). Also available are: "LM" (linear, distance-based model), "RBFN" Radial Basis Function Network.

modelSettings

List of settings for model building, passed on as the control argument to the model training functions modelKriging, modelLinear, modelRBFN.

infill

This parameter specifies a function to be used for the infill criterion (e.g., the default is expected improvement infillExpectedImprovement). To use no specific infill criterion this has to be set to NA. Infill criteria are only used with models that may provide some error estimate with predictions.

list of samples in input space

matrix, column vector of observations for each sample