aclhs (Huong et al., 20xx) implements the autocorrelated conditioned Latin Hypercube Sampling algorithm for 1 dimensional (i.e., time-series) and 2 dimensional (i.e., spatial) data with independent and dependent variable values.
The latest stable version and development of aclhs is available at https://github.com/vargaslab/acLHS.
If you use aclhs for any published work, please:
- Cite Huong et al. (2024) in the references.
- Add a URL in the footnote with the link to the
aclhspackage GitHub: https://github.com/vargaslab/acLHS.
Le V. H., Vargas R. (2024). "An Autocorrelated Conditioned Latin Hypercube Sampling Method for Temporal or Spatial Sampling and Predictions." Computers & Geosciences, 184, 105539. https://doi:10.1016/j.cageo.2024.105539.