Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.1.4.1
Date: 2019-12-05
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois@inria.fr>
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: http://github.com/mbinois/GPareto
BugReports: http://github.com/mbinois/GPareto/issues
RoxygenNote: 7.0.2
NeedsCompilation: yes
Packaged: 2020-03-30 11:41:05 UTC; hornik
Date/Publication: 2020-04-01 11:25:31 UTC
Built: R 4.0.0; x86_64-w64-mingw32; 2020-04-10 09:34:18 UTC; windows
Archs: i386, x64
