Kriging Methods for Computer Experiments

Estimation, validation and prediction of kriging models.


Package: DiceKriging
Type: Package
Version: 1.5.6
Date: 2018-10-08
License: GPL-2 | GPL-3


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


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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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O. Roustant, D. Ginsbourger and Yves Deville (2012), DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization, Journal of Statistical Software, 51(1), 1-55, http://www.jstatsoft.org/v51/i01/.

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  • DiceKriging
Documentation reproduced from package DiceKriging, version 1.5.6, License: GPL-2 | GPL-3

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