Package: GPareto
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.0.2
Date: 2016-03-01
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 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 (>= 1.5.3), emoa, methods
Imports: Rcpp (>= 0.11.1), rgenoud, pbivnorm, pso, randtoolbox,
        KrigInv, MASS, DiceDesign (>= 1.4), ks
Suggests: knitr
VignetteBuilder: knitr
LinkingTo: Rcpp
Repository: CRAN
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-03-01 15:31:54 UTC; vpicheny
Date/Publication: 2016-03-02 02:13:50
