GPfit

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).


References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
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  1. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  2. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  3. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  4. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  5. Vernon, Ian; Jackson, Samuel E.; Cumming, Jonathan A.: Known boundary emulation of complex computer models (2019)
  6. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  7. Leatherman, Erin R.; Santner, Thomas J.; Dean, Angela M.: Computer experiment designs for accurate prediction (2018)
  8. Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
  9. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH
  10. Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015) not zbMATH
  11. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015) not zbMATH
  12. Gramacy, Robert B.; Bingham, Derek; Holloway, James Paul; Grosskopf, Michael J.; Kuranz, Carolyn C.; Rutter, Erica; Trantham, Matt; Drake, R. Paul: Calibrating a large computer experiment simulating radiative shock hydrodynamics (2015)
  13. Jesús Palomo; Rui Paulo; Gonzalo García-Donato: SAVE: An R Package for the Statistical Analysis of Computer Models (2015) not zbMATH
  14. Butler, A.; Haynes, R. D.; Humphries, T. D.; Ranjan, P.: Efficient optimization of the likelihood function in Gaussian process modelling (2014)
  15. Blake MacDoanld, Hugh Chipman, Pritam Ranjan: GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm (2013) arXiv