A computationally stable approach of fitting a Gaussian process (GP) model to simulator outputs. It is assumed that the input variables are continuous and the outputs are obtained from scalar valued deterministic computer simulator.

This package implements a slightly modified version of the regularized GP model proposed in Ranjan et al. (2011). For details see MacDonald et al. (2015). A new parameterization of the Gaussian correlation is used for the ease of optimization. This package uses a multi-start gradient based search algorithm for optimizing the deviance (negative 2*log-likelihood).

For a complete list of functions, use `library(help="GPfit")`

. The
main function for fitting the GP model is `GP_fit`

.

MacDonald, K.B., Ranjan, P. and Chipman, H. (2015). GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64(12), 1-23. http://www.jstatsoft.org/v64/i12/

Ranjan, P., Haynes, R., and Karsten, R. (2011). A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data, Technometrics, 53(4), 366 - 378.

Santner, T.J., Williams, B., and Notz, W. (2003), The design and analysis of computer experiments, Springer Verlag, New York.