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Gaussian process (GP) models are commonly used statistical metamodels foremulating expensive computer simulators. Fitting a GP model can be numericallyunstable if any pair of design points in the input space are close together.Ranjan, Haynes, and Karsten (2011) proposed a computationally stable approachfor fitting GP models to deterministic computer simulators. They used a geneticalgorithm based approach that is robust but computationally intensive formaximizing the likelihood. This paper implements a slightly modified version ofthe model proposed by Ranjan et al. (2011), as the new R package GPfit. A novelparameterization of the spatial correlation function and a new multi-startgradient based optimization algorithm yield optimization that is robust andtypically faster than the genetic algorithm based approach. We present twoexamples with R codes to illustrate the usage of the main functions in GPfit.Several test functions are used for performance comparison with a popular Rpackage mlegp. GPfit is a free software and distributed under the generalpublic license, as part of the R software project (R Development Core Team2012).

GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm