Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.1.2
Date: 2018-12-07
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
URL: http://github.com/mbinois/GPareto
BugReports: http://github.com/mbinois/GPareto/issues
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2018-12-09 21:05:10 UTC; mickael
Date/Publication: 2018-12-10 10:10:03 UTC
