Package: CompExpDes 1.0.3

CompExpDes: Computer Experiment Designs

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) <doi:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. 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 attained the lower bound of Discrete Discrepancy measure.

Authors:Ashutosh Dalal [aut, cre], Cini Varghese [aut, ctb], Rajender Parsad [aut, ctb], Mohd Harun [aut, ctb]

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CompExpDes/json (API)

# Install 'CompExpDes' in R:
install.packages('CompExpDes', repos = c('https://ashutoshdalal97.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

9 exports 0.36 score 0 dependencies 11 scripts 487 downloads

Last updated 14 days agofrom:56ebaa3c93. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 04 2024
R-4.5-winOKSep 04 2024
R-4.5-linuxOKSep 04 2024
R-4.4-winOKSep 04 2024
R-4.4-macOKSep 04 2024
R-4.3-winOKSep 04 2024
R-4.3-macOKSep 04 2024

Exports:Discrete_DiscrepancyLHDs_ILHDs_IIMACMaxpro_MeasurePhipMeasureUDesigns_IUDesigns_IIUDesigns_III

Dependencies: