  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 fitting 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), as the new R package GPfit. A novel
parameterization of the spatial correlation function and a new multi-start
gradient based optimization algorithm yield optimization that is robust and
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 performance comparison with a popular R
package mlegp. GPfit is a free software and distributed under the general
public license, as part of the R software project (R Development Core Team
2012).
