Package: GPareto 1.1.9

GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Authors:Mickael Binois, Victor Picheny

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GPareto.pdf |GPareto.html✨
GPareto/json (API)
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Bug tracker:https://github.com/mbinois/gpareto/issues

On CRAN:

36 exports 16 stars 2.17 score 54 dependencies 1 dependents 38 scripts 776 downloads

Last updated 8 months agofrom:fe43d0dd5d. Checks:OK: 9. Indexed: yes.

Exports:checkPredictCPFcrit_EHIcrit_EMIcrit_optimizercrit_qEHIcrit_SMScrit_SURDTLZ1DTLZ2DTLZ3DTLZ7easyGParetoptimfastfungetDesignGParetoptimintegration_design_optimMOP2MOP3nonDomSetOKA1P1P2ParetoSetDensityplot_uncertaintyplotGParetoplotParetoEmpplotParetoGridplotSymDevFunplotSymDifRNPpredict_kmsZDT1ZDT2ZDT3ZDT4ZDT6

Dependencies:anMCbase64encbslibcachemcliDiceDesignDiceKrigingdigestemoaevaluatefastmapFNNfontawesomefsgluehighrhtmltoolshtmlwidgetsjquerylibjsonlitekernlabKernSmoothknitrKrigInvkslatticelifecyclemagrittrMASSMatrixmclustmemoisemgcvmimemulticoolmvtnormnlmepbivnormpracmapsoR6randtoolboxrappdirsRcppRcppArmadillorgenoudrglrlangrmarkdownrngWELLsasstinytexxfunyaml