Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
An efficient methodology for modeling complex computer codes with Gaussian processes
Computational Statistics & Data Analysis
Gaussian processes and limiting linear models
Computational Statistics & Data Analysis
Sequential design of computer experiments for the estimation of a probability of failure
Statistics and Computing
Editorial: Special issue on statistical algorithms and software in R
Computational Statistics & Data Analysis
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Several strategies relying on kriging have recently been proposed for adaptively estimating contour lines and excursion sets of functions under severely limited evaluation budget. The recently released R package KrigInv is presented and offers a sound implementation of various sampling criteria for those kinds of inverse problems. KrigInv is based on the DiceKriging package, and thus benefits from a number of options concerning the underlying kriging models. Six implemented sampling criteria are detailed in a tutorial and illustrated with graphical examples. Different functionalities of KrigInv are gradually explained. Additionally, two recently proposed criteria for batch-sequential inversion are presented, enabling advanced users to distribute function evaluations in parallel on clusters or clouds of machines. Finally, auxiliary problems are discussed. These include the fine tuning of numerical integration and optimization procedures used within the computation and the optimization of the considered criteria.