DiceKriging

R package DiceKriging: Kriging methods for computer experiments. Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.

Keywords for this software

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References in zbMATH (referenced in 61 articles , 1 standard article )

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  1. Binois, Mickaël; Ginsbourger, David; Roustant, Olivier: On the choice of the low-dimensional domain for global optimization via random embeddings (2020)
  2. El Amri, Mohamed Reda; Helbert, Céline; Lepreux, Olivier; Zuniga, Miguel Munoz; Prieur, Clémentine; Sinoquet, Delphine: Data-driven stochastic inversion via functional quantization (2020)
  3. Gahrooei, Mostafa Reisi; Yan, Hao; Paynabar, Kamran: Comments on: “On active learning methods for manifold data” (2020)
  4. Xie, Fangzheng; Xu, Yanxun: Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models (2020)
  5. Yang, F.; Lin, C. Devon; Ranjan, P.: Global fitting of the response surface via estimating multiple contours of a simulator (2020)
  6. Bachoc, François; Bevilacqua, Moreno; Velandia, Daira: Composite likelihood estimation for a Gaussian process under fixed domain asymptotics (2019)
  7. Bachoc, François; Lagnoux, Agnès; López-Lopera, Andrés F.: Maximum likelihood estimation for Gaussian processes under inequality constraints (2019)
  8. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  9. Hammond, Janelle K.; Chakir, R.; Bourquin, F.; Maday, Y.: PBDW: a non-intrusive reduced basis data assimilation method and its application to an urban dispersion modeling framework (2019)
  10. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  11. Mohammadi, Hossein; Challenor, Peter; Goodfellow, Marc: Emulating dynamic non-linear simulators using Gaussian processes (2019)
  12. Picheny, Victor; Binois, Mickael; Habbal, Abderrahmane: A Bayesian optimization approach to find Nash equilibria (2019)
  13. Salem, Malek Ben; Bachoc, François; Roustant, Olivier; Gamboa, Fabrice; Tomaso, Lionel: Gaussian process-based dimension reduction for goal-oriented sequential design (2019)
  14. Sambakhé, Diariétou; Rouan, Lauriane; Bacro, Jean-Noël; Gozé, Eric: Conditional optimization of a noisy function using a kriging metamodel (2019)
  15. Sun, Furong; Gramacy, Robert B.; Haaland, Benjamin; Lawrence, Earl; Walker, Andrew: Emulating satellite drag from large simulation experiments (2019)
  16. Tran, Anh; Sun, Jing; Furlan, John M.; Pagalthivarthi, Krishnan V.; Visintainer, Robert J.; Wang, Yan: pBO-2GP-3B: a batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics (2019)
  17. Vollert, Natalie; Ortner, Michael; Pilz, Jürgen: Robust additive Gaussian process models using reference priors and cut-off-designs (2019)
  18. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  19. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  20. Gu, Mengyang; Wang, Long: Scaled Gaussian stochastic process for computer model calibration and prediction (2018)

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