_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-clhs 0.9.2
Propagated dependencies: r-sf@1.0-23 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-raster@3.6-32 r-plyr@1.8.9 r-ggplot2@4.0.1 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/pierreroudier/clhs/
Licenses: GPL 2+
Synopsis: Conditioned Latin Hypercube Sampling
Description:

Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).

Total results: 1