Package: GPareto
Type: Package
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.1.0
Date: 2017-06-23
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: 6.0.1
NeedsCompilation: yes
Packaged: 2017-06-28 13:32:21 UTC; mickael
Date/Publication: 2017-06-29 05:30:09 UTC
