_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-gpareto 1.1.8
Propagated dependencies: r-rgl@1.3.18 r-rgenoud@5.9-0.11 r-rcpp@1.0.14 r-randtoolbox@2.0.5 r-pso@1.0.4 r-pbivnorm@0.6.0 r-mass@7.3-65 r-ks@1.15.1 r-kriginv@1.4.2 r-emoa@0.5-3 r-dicekriging@1.6.0 r-dicedesign@1.10
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/mbinois/GPareto
Licenses: GPL 3
Synopsis: Gaussian Processes for Pareto Front Estimation and Optimization
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.

Total results: 1