Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.0.0
Date: 2014-12-17
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: Rcpp (>= 0.11.1), rgenoud, pbivnorm, pso, randtoolbox,
        KrigInv, MASS
Suggests: DiceDesign (>= 1.4)
LinkingTo: Rcpp
Packaged: 2014-12-17 09:42:42 UTC; a073501
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-12-17 13:08:28
Built: R 3.1.2; x86_64-apple-darwin10.8.0; 2014-12-18 13:52:51 UTC; unix
Archs: GPareto.so.dSYM
