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LHD (version 1.2.0)

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 maximin distance LHDs, maximum projection LHDs, and nearly orthogonal 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

203

Version

1.2.0

License

MIT + file LICENSE

Maintainer

Hongzhi Wang

Last Published

September 17th, 2020

Functions in LHD (1.2.0)

LaPSO

Particle Swarm Optimization for LHD
LPWT

Linear Permuted Williams Transformation
GLP

Good Lattice Point Design
AvgAbsCor

Calculate the Average Absolute Correlation
GA

Genetic Algorithm for LHD
exchange

Exchange two random elements
dij

Calculate the Inter-site Distance
WT

Williams Transformation
phi_p

Calculate the phi_p Criterion
SLHD

Sliced Latin Hypercube Design (SLHD)
SA2008

Simulated Annealing for LHD with Multi-objective Optimization Approach
OLHD1998

Orthogonal Latin Hypercube Design
SA

Simulated Annealing for LHD
rLHD

Generate a random Latin Hypercube Design (LHD)
FastMmLHD

Fast Maximin Distance LHD
OA2LHD

Transfer an Orthogonal Array (OA) into an LHD
OASA

Orthogonal-Array-Based Simulated Annealing
MWT

Modified Williams Transformation
LOO

Leave-one-out Method
MaxProCriterion

Calculate the Maximum Projection Criterion
MaxAbsCor

Calculate the Maximum Absolute Correlation