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LHD (version 1.1.0)

Latin Hypercube Designs (LHDs) Algorithms

Description

Contains different algorithms for efficient Latin Hypercube Designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating nearly orthogonal LHDs, maximin distance LHDs, and maximum projection LHDs. Documentation for each algorithm includes useful information and explanation along with corresponding references. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.

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Version

Install

install.packages('LHD')

Monthly Downloads

293

Version

1.1.0

License

MIT + file LICENSE

Maintainer

Hongzhi Wang

Last Published

June 2nd, 2020

Functions in LHD (1.1.0)

SA2008

Simulated Annealing for LHD with Multi-objective Optimization Approach
OASA

Orthogonal-Array-Based Simulated Annealing
SLHD

Sliced Latin Hypercube Design (SLHD)
GA

Genetic Algorithm for LHD
MaxAbsCor

Calculate the Maximum Absolute Correlation
AvgAbsCor

Calculate the Average Absolute Correlation
LaPSO

Particle Swarm Optimization for LHD
phi_p

Calculate the phi_p Criterion
SA

Simulated Annealing for LHD
MaxProCriterion

Calculate the Maximum Projection Criterion
dij

Calculate the Inter-site Distance
exchange

Exchange two random elements
OA2LHD

Transfer an Orthogonal Array (OA) into an LHD
rLHD

Generate a random Latin Hypercube Design (LHD)