Autocorrelated Conditioned Latin Hypercube Sampling
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), . For acLHS, see Le
and Vargas (2024) .