Package: GPareto
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.0.3
Date: 2016-11-09
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois@chicagobooth.edu>
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 (>= 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: 2016-11-10 23:43:55 UTC; mickael
Date/Publication: 2016-11-11 17:36:23
Built: R 3.2.5; x86_64-w64-mingw32; 2017-04-20 08:47:23 UTC; windows
Archs: i386, x64
