dataset · 2025
aclhs: Autocorrelated Conditioned Latin Hypercube Sampling
Cited by 0Open access
Abstract
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), <<a href="https://doi.org/10.1016%2Fj.cageo.2005.12.009" target="_top">doi:10.1016/j.cageo.2005.12.009</a>>. For acLHS, see Le and Vargas (2024) <<a href="https://doi.org/10.1016%2Fj.cageo.2024.105539" target="_top">doi:10.1016/j.cageo.2024.105539</a>>.
Cite this paper
Le, V. H., & Vargas, R. (2025). aclhs: Autocorrelated Conditioned Latin Hypercube Sampling. https://doi.org/10.32614/cran.package.aclhs
Le, Van Huong, and Rodrigo Vargas. “aclhs: Autocorrelated Conditioned Latin Hypercube Sampling.” 2025, https://doi.org/10.32614/cran.package.aclhs.
Le, Van Huong, and Rodrigo Vargas. 2025. “aclhs: Autocorrelated Conditioned Latin Hypercube Sampling.” https://doi.org/10.32614/cran.package.aclhs.
Le, V.H. and Vargas, R. (2025) ‘aclhs: Autocorrelated Conditioned Latin Hypercube Sampling’. Available at: https://doi.org/10.32614/cran.package.aclhs.
@misc{le2025aclhs,
title = {{aclhs: Autocorrelated Conditioned Latin Hypercube Sampling}},
author = {Le, Van Huong and Vargas, Rodrigo},
year = {2025},
doi = {10.32614/cran.package.aclhs}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.