Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.1.9
Date: 2025-08-25
Authors@R: c(person(given = "Mickael",
                        family = "Binois",
                        role = c("aut", "cre"),
                        email = "mickael.binois@inria.fr",
                        comment = c(ORCID = "0000-0002-7225-1680")),
                 person(given = c("Victor"),
                        family = "Picheny",
                        role = "aut"))
Description: 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.
License: GPL-3
Depends: DiceKriging, emoa
Imports: Rcpp (>= 0.12.15), methods, rgenoud, pbivnorm, pso,
        randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl
Suggests: knitr
VignetteBuilder: knitr
LinkingTo: Rcpp
Repository: CRAN
URL: https://github.com/mbinois/GPareto
BugReports: https://github.com/mbinois/GPareto/issues
RoxygenNote: 7.3.2
NeedsCompilation: yes
Packaged: 2025-08-25 07:56:50 UTC; mbinois
Author: Mickael Binois [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-7225-1680>),
  Victor Picheny [aut]
Maintainer: Mickael Binois <mickael.binois@inria.fr>
Date/Publication: 2025-08-25 09:10:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-29 03:31:42 UTC; unix
Archs: GPareto.so.dSYM
