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aclhs  

Autocorrelated Conditioned Latin Hypercube Sampling
View on CRAN: Click here


Download and install aclhs package within the R console
Install from CRAN:
install.packages("aclhs")

Install from Github:
library("remotes")
install_github("cran/aclhs")

Install by package version:
library("remotes")
install_version("aclhs", "1.0.1")



Attach the package and use:
library("aclhs")
Maintained by
Gabriel Laboy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-11-05
Latest Update: 2025-11-05
Description:
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>.
How to cite:
Gabriel Laboy (2025). aclhs: Autocorrelated Conditioned Latin Hypercube Sampling. R package version 1.0.1, https://cran.r-project.org/web/packages/aclhs. Accessed 25 Jun. 2026.
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