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DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

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  • Roustant, Olivier
  • Ginsbourger, David
  • Deville, Yves

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.

Suggested Citation

  • Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    • Handle: RePEc:jss:jstsof:v:051:i01
    DOI: http://hdl.handle.net/10.18637/jss.v051.i01
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    References listed on IDEAS

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    1. Hankin, Robin K. S., 2005. "Introducing BACCO, an R Bundle for Bayesian Analysis of Computer Code Output," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i16).
    2. Mebane Jr., Walter R. & Sekhon, Jasjeet S., 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i11).
    3. Gramacy, Robert B. & Taddy, Matthew Alan, 2010. "Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i06).
    4. C. Helbert & D. Dupuy & L. Carraro, 2009. "Assessment of uncertainty in computer experiments from Universal to Bayesian Kriging," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 99-113, March.
    5. Gramacy, Robert B., 2007. "tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i09).
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