Package: CompExpDes 1.0.5

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 always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.

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'))

Peer review:

On CRAN:

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

1.78 score 12 scripts 343 downloads 11 exports 0 dependencies

Last updated 19 days agofrom:ff9c873da6. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:Best_ModelDiscrete_DiscrepancyLHDs_ILHDs_IIMACMaxpro_MeasureMeeting_NumberPhipMeasureUDesigns_IUDesigns_IIUDesigns_III

Dependencies: