Learn R Programming

CompExpDes (version 1.0.7)

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.

Copy Link

Version

Install

install.packages('CompExpDes')

Monthly Downloads

413

Version

1.0.7

License

GPL (>= 2)

Maintainer

Ashutosh Dalal

Last Published

March 29th, 2025

Functions in CompExpDes (1.0.7)

Best_Model

Find Best Model
Meeting_Number

Maximum Coincidence (or Meeting) numbers between rows
SLHDs

Sliced Latin Hypercube Designs with Equal Size of Slices
MAC

Maximum Absolute Correlation
Maxpro_Measure

Measure of Maxpro criterion
UDesigns_I

Orthogonal Uniform Designs with Two Factors
wtLHDs

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

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

Measure of Discrete Discrepancy
NOLHDs

Nearly Orthogonal Latin Hypercube Designs for Flexible Levels and Factors
OLHDs_2F

Two Factor Orthogonal Latin Hypercube Designs
PhipMeasure

Phi_p criterion
UDesigns_II

Uniform Designs with Multiple Factors with Minimal Runs
UDesigns_III

Nearly Orthogonal Uniform Designs for Two and Four Factors