Package: GPareto
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.0.1
Date: 2015-06-22
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois@mines-stetienne.fr>
Description: Gaussian process regression models, a.k.a. kriging models, are
    applied to global multiobjective optimization of black-box functions.
    Multiobjective Expected Improvement and Stepwise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
License: GPL-3
Depends: DiceKriging (>= 1.5.3), emoa, methods
Imports: stats, grDevices, graphics, Rcpp (>= 0.11.1), rgenoud,
        pbivnorm, pso, randtoolbox, KrigInv, MASS
Suggests: DiceDesign (>= 1.4)
LinkingTo: Rcpp
Repository: CRAN
NeedsCompilation: yes
Packaged: 2015-07-03 09:07:26 UTC; binois
Date/Publication: 2015-07-03 12:50:54
