GPfit: An R package for fitting a Gaussian process model to deterministic simulator outputs

Gaussian process (gp) models are commonly used statistical metamodels for emulating expensive computer simulators. fitting a gp model can be numerically unstable if any pair of design points in the input space are close together. ranjan,haynes,and karsten (2011) proposed a computationally stable approach for _tting gp models to deterministic computer simulators. they used a genetic algorithm based approach that is robust but computationally intensive for maximizing the likelihood. this paper implements a slightly modified version of the model proposed by ranjan et al. (2011) in the r package gpfi t. a novel parameterization of the spatial correlation function and a clustering based multi- start gradient based optimization algorithm yield robust optimization that is typically faster than the genetic algorithm based approach. we present two examples with r codes to illustrate the usage of the main functions in gpfit. several test functions are used for pesrformance comparison with the popular r package mlegp. we also use gpfit for a real application,i.e.,for emulating the tidal kinetic energy model for the bay of fundy,nova scotia,canada. gpfit is free software and distributed under the general public license and available from the comprehensive r archive network. © 2015,american statistical association. all rights reserved.