Package: CompExpDes 1.0.9
CompExpDes: Designs for Computer Experimentations
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:
CompExpDes_1.0.9.tar.gz
CompExpDes_1.0.9.zip(r-4.7)CompExpDes_1.0.9.zip(r-4.6)CompExpDes_1.0.9.zip(r-4.5)
CompExpDes_1.0.9.tgz(r-4.6-any)CompExpDes_1.0.9.tgz(r-4.5-any)
CompExpDes_1.0.9.tar.gz(r-4.7-any)CompExpDes_1.0.9.tar.gz(r-4.6-any)
CompExpDes_1.0.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CompExpDes/json (API)
| # Install 'CompExpDes' in R: |
| install.packages('CompExpDes', repos = c('https://ashutoshdalal97.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:0bb4758b3f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 104 | ||
| source / vignettes | OK | 145 | ||
| linux-release-x86_64 | OK | 114 | ||
| macos-release-arm64 | OK | 109 | ||
| macos-oldrel-arm64 | OK | 91 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 79 | ||
| windows-oldrel | OK | 152 | ||
| wasm-release | OK | 125 |
Exports:Best_ModelDiscrete_DiscrepancyMACmax_coincidence_numberMaxpro_MeasureNOLHDsOLHDs_2FPhipMeasureSLHDsUDesigns_IUDesigns_IIUDesigns_IIIwtLHDswtLHDs_prime
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Find Best Model | Best_Model |
| Measure of Discrete Discrepancy | Discrete_Discrepancy |
| Maximum Absolute Correlation | MAC |
| Maximum Coincidence (or Meeting) numbers between rows | max_coincidence_number |
| Measure of Maxpro criterion | Maxpro_Measure |
| Nearly Orthogonal Latin Hypercube Designs for Flexible Levels and Factors | NOLHDs |
| Two Factor Orthogonal Latin Hypercube Designs | OLHDs_2F |
| Phi_p criterion | PhipMeasure |
| Sliced Latin Hypercube Designs with Equal Size of Slices | SLHDs |
| Orthogonal Uniform Designs with Two Factors | UDesigns_I |
| Uniform Designs with Multiple Factors with Minimal Runs | UDesigns_II |
| Nearly Orthogonal Uniform Designs for Two and Four Factors | UDesigns_III |
| Weighted Criteria-Based Latin Hypercube Designs (LHDs) for Any Numbers of Factors (>=2) | wtLHDs |
| Weighted Criteria-Based Latin Hypercube Designs (LHDs) for Prime Numbers | wtLHDs_prime |
