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LHD (version 0.1.2)

Latin Hypercube Designs (LHDs) Algorithms

Description

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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Version

Install

install.packages('LHD')

Monthly Downloads

293

Version

0.1.2

License

MIT + file LICENSE

Maintainer

Hongzhi Wang

Last Published

October 22nd, 2019

Functions in LHD (0.1.2)

dij

Calculate the Inter-site Distance
SA2008

Simulated Annealing for LHD with Multi-objective Optimization Approach
SLHD

Sliced Latin Hypercube Design (SLHD)
rLHD

Generate a random Latin Hypercube Design (LHD)
GA

Genetic Algorithm for LHD
OA2LHD

Transfer an Orthogonal Array (OA) into a LHD
phi_p

Calculate the phi_p Criterion
exchange

Exchange two random elements
OASA

Orthogonal-Array-Based Simulated Annealing
LaPSO

Particle Swarm Optimization for LHD
SA

Simulated Annealing for LHD