Latin Hypercube Designs (LHDs) Algorithms

Contains functions for finding space-filling Latin Hypercube Designs (LHDs), e.g. maximin distance LHDs. Unlike other packages, our package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, it is very useful in design of computer experiments. One advantage of our package is its comprehensiveness. It contains a variety of heuristic algorithms (and their modifications) for searching maximin distance LHDs. In addition to that, it also contains other useful tools for developing and constructing maximin distance LHDs. In the future, algebraic construction methods will be added. Please refer to the function documentations for the detailed references of each function. Among all the references we used, one reference should be highlighted here, which is Ruichen Jin, Wei Chen, Agus Sudjianto (2005) . They provided a new form of phi_p criterion, which does not lose the space-filling property and simultaneously reduces the computational complexity when evaluating (or re-evaluating) an LHD. Their new phi_p criterion is a fundamental component of our many functions. Besides, the computation nature of the new phi_p criterion enables our functions to have less CPU time.


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Reference manual

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install.packages("LHD")

0.1.2 by Hongzhi Wang, 2 months ago


Browse source code at https://github.com/cran/LHD


Authors: Hongzhi Wang , Qian Xiao , Abhyuday Mandal


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports stats

Suggests testthat, knitr, rmarkdown, devtools


See at CRAN