Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.1.1
Date: 2018-01-30
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mbinois@mcs.anl.gov>
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
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-01-31 22:22:04 UTC; mickael
Date/Publication: 2018-01-31 23:26:44 UTC
Built: R 3.3.3; x86_64-w64-mingw32; 2018-04-22 17:33:41 UTC; windows
Archs: i386, x64
