Autocorrelated Conditioned Latin Hypercube Sampling
Computes correlations between the original and aclhs-sampled data.
Set parameters for plotting.
Plots the acLHS samples distribution.
Plot the scatterplot of the acLHS subsamples.
Plot the univariate PDF for a column of acLHS-derived samples.
Plot the Variogram comparison of the acLHS subsamples.
Get subsample indices using the acLHS algorithm.
Set parameters for computing a Variogram.
Computes a score from three objective functions.
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>.