Gaussian Processes for Pareto Front Estimation and Optimization R package GPareto version 1.1.9 CRAN Task View: Optimization (https://CRAN.R-project.org/view=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. Software Depends: DiceKriging, emoa Imports: Rcpp (>= 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl LinkingTo: Rcpp Suggests: knitr Mickael Binois <mickael.binois@inria.fr> Comprehensive R Archive Network (CRAN) Mickael Binois [aut, cre] (ORCID: <https://orcid.org/0000-0002-7225-1680>), Victor Picheny [aut] GPL-3 2025-08-25 application/tgz https://CRAN.R-project.org/package=GPareto doi:10.32614/CRAN.package.GPareto