DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodelling and optimization - Archive ouverte HAL Access content directly
Journal Articles Journal of Statistical Software Year : 2012

DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodelling and optimization

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Abstract

We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimization, the functionalities of both packages are detailed and demonstrated in two distinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. We then put to the fore the implementation of sequential and parallel optimization strategies relying on the expected improvement criterion on the occasion of DiceOptim’s presentation. An appendix is dedicated to complementary mathematical and computational details.
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Dates and versions

emse-00741762 , version 1 (01-11-2022)

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Attribution - CC BY 4.0

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Olivier Roustant, David Ginsbourger, Yves Deville. DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodelling and optimization. Journal of Statistical Software, 2012, 51 (1), 54p. ⟨10.18637/jss.v051.i01⟩. ⟨emse-00741762⟩
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