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Conditioned Latin Hypercube Sampling in Python

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Conditioned Latin Hypercube Sampling in Python.

This code is based on the conditioned LHS method of Minasny & McBratney (2006). It follows some of the code from the R package clhs of Roudier et al.

In short, this code attempts to create a Latin Hypercube sample by selecting only from input data. It uses simulated annealing to force the sampling to converge more rapidly, and also allows for setting a stopping criterion on the objective function described in Minasny & McBratney (2006).

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