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>).">

clhs: Conditioned Latin Hypercube Sampling (original) (raw)

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>).

Version: 0.9.0
Depends: R (≥ 2.14.0)
Imports: utils, methods, grid, ggplot2, sp, sf, raster, reshape2, plyr, cluster, Rcpp
LinkingTo: RcppArmadillo, Rcpp
Suggests: knitr, rmarkdown, testthat
Published: 2021-10-14
DOI: 10.32614/CRAN.package.clhs
Author: Pierre Roudier [aut, cre], Colby Brugnard [ctb], Dylan Beaudette [ctb], Benjamin Louis [ctb], Kiri Daust [ctb], David Clifford [ctb]
Maintainer: Pierre Roudier
BugReports: https://github.com/pierreroudier/clhs/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/pierreroudier/clhs/
NeedsCompilation: yes
Citation: clhs citation info
Materials: README NEWS
CRAN checks: clhs results

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