aclhs: Autocorrelated Conditioned Latin Hypercube Sampling

Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), <doi:10.1016/j.cageo.2005.12.009>. For acLHS, see Le and Vargas (2024) <doi:10.1016/j.cageo.2024.105539>.

Version:	1.0.1
Depends:	R (≥ 3.5)
Imports:	DEoptim (≥ 2.2.8), geoR (≥ 1.9.6), graphics (≥ 4.5.1), stats (≥ 4.5.1), utils (≥ 4.5.1)
Suggests:	testthat (≥ 3.0.0)
Published:	2025-11-05
DOI:	10.32614/CRAN.package.aclhs
Author:	Van Huong Le [aut, ctb], Rodrigo Vargas [aut], Gabriel Laboy [ctb, cre]
Maintainer:	Gabriel Laboy <glaboy1 at asu.edu>
BugReports:	https://github.com/vargaslab/acLHS/issues
License:	MIT + file LICENSE
URL:	https://github.com/vargaslab/acLHS
NeedsCompilation:	no
Citation:	aclhs citation info
CRAN checks:	aclhs results

Documentation:

Reference manual:

aclhs.html , aclhs.pdf

Downloads:

Package source:	aclhs_1.0.1.tar.gz
Windows binaries:	r-devel: aclhs_1.0.1.zip, r-release: aclhs_1.0.1.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): aclhs_1.0.1.tgz, r-oldrel (x86_64): not available

Linking:

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