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CompExpDes (version 1.0.9)

Designs for Computer Experimentations

Description

In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) and Fang (1980) . In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.

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Version

Install

install.packages('CompExpDes')

Monthly Downloads

273

Version

1.0.9

License

GPL (>= 2)

Maintainer

Ashutosh Dalal

Last Published

September 18th, 2025

Functions in CompExpDes (1.0.9)

NOLHDs

Nearly Orthogonal Latin Hypercube Designs for Flexible Levels and Factors
max_coincidence_number

Maximum Coincidence (or Meeting) numbers between rows
OLHDs_2F

Two Factor Orthogonal Latin Hypercube Designs
wtLHDs_prime

Weighted Criteria-Based Latin Hypercube Designs (LHDs) for Prime Numbers
wtLHDs

Weighted Criteria-Based Latin Hypercube Designs (LHDs) for Any Numbers of Factors (>=2)
Best_Model

Find Best Model
SLHDs

Sliced Latin Hypercube Designs with Equal Size of Slices
UDesigns_III

Nearly Orthogonal Uniform Designs for Two and Four Factors
UDesigns_I

Orthogonal Uniform Designs with Two Factors
Maxpro_Measure

Measure of Maxpro criterion
PhipMeasure

Phi_p criterion
Discrete_Discrepancy

Measure of Discrete Discrepancy
UDesigns_II

Uniform Designs with Multiple Factors with Minimal Runs
MAC

Maximum Absolute Correlation