Package: GPareto 1.2.0

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 [aut, cre], Victor Picheny [aut]

GPareto_1.2.0.tar.gz
GPareto_1.2.0.zip(r-4.7)GPareto_1.2.0.zip(r-4.6)GPareto_1.2.0.zip(r-4.5)
GPareto_1.2.0.tgz(r-4.6-x86_64)GPareto_1.2.0.tgz(r-4.6-arm64)GPareto_1.2.0.tgz(r-4.5-x86_64)GPareto_1.2.0.tgz(r-4.5-arm64)
GPareto_1.2.0.tar.gz(r-4.7-arm64)GPareto_1.2.0.tar.gz(r-4.7-x86_64)GPareto_1.2.0.tar.gz(r-4.6-arm64)GPareto_1.2.0.tar.gz(r-4.6-x86_64)
GPareto_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GPareto/json (API)
NEWS

# Install 'GPareto' in R:
install.packages('GPareto', repos = c('https://mbinois.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mbinois/gpareto/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

7.05 score 18 stars 1 packages 43 scripts 3.2k downloads 36 exports 56 dependencies

Last updated from:51365eed6b. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK237
linux-devel-x86_64OK220
source / vignettesOK478
linux-release-arm64OK210
linux-release-x86_64OK252
macos-release-arm64OK140
macos-release-x86_64OK244
macos-oldrel-arm64OK125
macos-oldrel-x86_64OK231
windows-develOK203
windows-releaseOK163
windows-oldrelOK152
wasm-releaseOK162

Exports:checkPredictCPFcrit_EHIcrit_EMIcrit_optimizercrit_qEHIcrit_SMScrit_SURDTLZ1DTLZ2DTLZ3DTLZ7easyGParetoptimfastfungetDesignGParetoptimintegration_design_optimMOP2MOP3nonDomSetOKA1P1P2ParetoSetDensityplot_uncertaintyplotGParetoplotParetoEmpplotParetoGridplotSymDevFunplotSymDifRNPpredict_kmsZDT1ZDT2ZDT3ZDT4ZDT6

Dependencies:anMCbase64encbslibcachemcliDiceDesignDiceKrigingdigestevaluatefastmapFNNfontawesomefshighrhtmltoolshtmlwidgetsjquerylibjsonlitekernlabKernSmoothknitrKrigInvkslatticelifecyclemagrittrMASSMatrixmatrixStatsmclustmemoisemgcvmimemoocoremulticoolmvtnormnlmepbivnormpracmapsoR6randtoolboxrappdirsrbibutilsRcppRcppArmadilloRdpackrgenoudrglrlangrmarkdownrngWELLsasstinytexxfunyaml

a guide to the GPareto package

Rendered fromGPareto_vignette.Rnwusingknitr::knitron May 09 2026.

Last update: 2026-02-08
Started: 2018-02-04

Readme and manuals

Help Manual

Help pageTopics
Package GParetoGPareto-package GPareto
Prevention of numerical instability for a new observationcheckPredict
Conditional Pareto Front simulationsCPF
Expected Hypervolume Improvement with m objectivescrit_EHI
Expected Maximin Improvement with m objectivescrit_EMI
Maximization of multiobjective infill criterioncrit_optimizer
Batch Expected Hypervolume Improvement with m objectivescrit_qEHI
Analytical expression of the SMS-EGO criterion with m>1 objectivescrit_SMS
Analytical expression of the SUR criterion for two or three objectives.crit_SUR
EGO algorithm for multiobjective optimizationeasyGParetoptim
Fast-to-evaluate function wrapperfastfun
Get design corresponding to an objective targetgetDesign
Sequential multi-objective Expected Improvement maximization and model re-estimation, with a number of iterations fixed in advance by the userGParetoptim
Function to build integration points (for the SUR criterion)integration_design_optim
Non-dominated points with respect to a referencenonDomSet
Estimation of Pareto set densityParetoSetDensity
Plot uncertaintyplot_uncertainty
Plot multi-objective optimization results and post-processingplotGPareto
Pareto front visualizationplotParetoEmp
Visualisation of Pareto front and setplotParetoGrid
Display the Symmetric Deviation FunctionplotSymDevFun
Symmetrical difference of RNP setsplotSymDifRNP
Predict function for list of 'km' models.predict_kms
Test functions of xDTLZ1 DTLZ2 DTLZ3 DTLZ7 MOP2 MOP3 OKA1 P1 P2 ZDT1 ZDT2 ZDT3 ZDT4 ZDT6