Package: CompExpDes 1.0.6

Ashutosh Dalal

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]

CompExpDes_1.0.6.tar.gz
CompExpDes_1.0.6.tar.gz(r-4.5-noble)CompExpDes_1.0.6.tar.gz(r-4.4-noble)
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CompExpDes.pdf |CompExpDes.html
CompExpDes/json (API)

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

Peer review:

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

1.95 score 15 scripts 546 downloads 14 exports 0 dependencies

Last updated 29 days agofrom:55a911f000. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 28 2024
R-4.5-linuxOKNov 28 2024

Exports:Best_ModelDiscrete_DiscrepancyLHDs_ILHDs_IIMACMaxpro_MeasureMeeting_NumberNOLHDsOLHDs_2FPhipMeasureSLHDsUDesigns_IUDesigns_IIUDesigns_III

Dependencies: