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 21 articles , 1 standard article )

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  1. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  2. Xiao, Qian; Mandal, Abhyuday; Lin, C. Devon; Deng, Xinwei: EzGP: easy-to-interpret Gaussian process models for computer experiments with both quantitative and qualitative factors (2021)
  3. Yu, Xuejun; Nott, David J.; Tran, Minh-Ngoc; Klein, Nadja: Assessment and adjustment of approximate inference algorithms using the law of total variance (2021)
  4. Hung, Ying-Chao; Michailidis, George; PakHai Lok, Horace: Locating infinite discontinuities in computer experiments (2020)
  5. Jackson, Samuel E.; Vernon, Ian; Liu, Junli; Lindsey, Keith: Understanding hormonal crosstalk in \textitArabidopsisroot development via emulation and history matching (2020)
  6. Sung, Chih-Li; Wang, Wenjia; Plumlee, Matthew; Haaland, Benjamin: Multiresolution functional ANOVA for large-scale, many-input computer experiments (2020)
  7. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  8. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  9. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  10. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  11. Vernon, Ian; Jackson, Samuel E.; Cumming, Jonathan A.: Known boundary emulation of complex computer models (2019)
  12. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  13. Leatherman, Erin R.; Santner, Thomas J.; Dean, Angela M.: Computer experiment designs for accurate prediction (2018)
  14. Mathieu Carmassi; Pierre Barbillon; Matthieu Chiodetti; Merlin Keller; Eric Parent: CaliCo: a R package for Bayesian calibration (2018) arXiv
  15. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH
  16. Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015) not zbMATH
  17. Christopher Paciorek; Benjamin Lipshitz; Wei Zhuo; Prabhat; Cari G. Kaufman; Rollin Thomas: Parallelizing Gaussian Process Calculations in R (2015) not zbMATH
  18. 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)
  19. Jesús Palomo; Rui Paulo; Gonzalo García-Donato: SAVE: An R Package for the Statistical Analysis of Computer Models (2015) not zbMATH
  20. Butler, A.; Haynes, R. D.; Humphries, T. D.; Ranjan, P.: Efficient optimization of the likelihood function in Gaussian process modelling (2014)

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