| DiceKriging-package {DiceKriging} | R Documentation |
Kriging Methods for Computer Experiments
Description
Estimation, validation and prediction of kriging models.
Details
| Package: | DiceKriging |
| Type: | Package |
| Version: | 1.6.0 |
| Date: | 2021-02-23 |
| License: | GPL-2 | GPL-3 |
Note
A previous version of this package was conducted within the frame of the DICE (Deep Inside Computer Experiments) Consortium between ARMINES, Renault, EDF, IRSN, ONERA and TOTAL S.A. (http://dice.emse.fr/).
The authors wish to thank Laurent Carraro, Delphine Dupuy and Celine Helbert for fruitful discussions about the structure of the code, and Francois Bachoc for his participation in validation and estimation by leave-one-out. They also thank Gregory Six and Gilles Pujol for their advices on practical implementation issues, as well as the DICE members for useful feedbacks.
Package rgenoud >= 5.8-2.0 is recommended.
Important functions or methods:
km | Estimation (or definition) of a kriging model with unknown (known) parameters |
predict | Prediction of the objective function at new points using a kriging model (Simple and |
| Universal Kriging) | |
plot | Plot diagnostic for a kriging model (leave-one-out) |
simulate | Simulation of kriging models |
Author(s)
Olivier Roustant, David Ginsbourger, Yves Deville. Contributors: C. Chevalier, Y. Richet.
(maintainer: Olivier Roustant roustant@insa-toulouse.fr)
References
F. Bachoc (2013), Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification. Computational Statistics and Data Analysis, 66, 55-69. http://www.lpma.math.upmc.fr/pageperso/bachoc/publications.html
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D. Ginsbourger, D. Dupuy, A. Badea, O. Roustant, and L. Carraro (2009), A note on the choice and the estimation of kriging models for the analysis of deterministic computer experiments, Applied Stochastic Models for Business and Industry, 25 no. 2, 115-131.
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