swMATH ID: 14044
Software Authors: Blake MacDoanld, Hugh Chipman, Pritam Ranjan
Description: R package GPfit: Gaussian Processes Modeling. A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator. 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).
Homepage: https://cran.r-project.org/web/packages/GPfit/index.html
Dependencies: R
Related Software: R; mlegp; laGP; tgp; DiceKriging; lhs; GPy; Rcpp; Matlab; rgenoud; DiceOptim; DiceDesign; SAVE; EGO; MaxPro; NOMAD; KrigInv; colorspace; kergp; CRAN
Referenced in: 12 Publications

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