Package: CompExpDes 1.0.6
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:
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)
CompExpDes_1.0.6.tgz(r-4.4-emscripten)CompExpDes_1.0.6.tgz(r-4.3-emscripten)
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 29 days agofrom:55a911f000. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 28 2024 |
R-4.5-linux | OK | Nov 28 2024 |
Exports:Best_ModelDiscrete_DiscrepancyLHDs_ILHDs_IIMACMaxpro_MeasureMeeting_NumberNOLHDsOLHDs_2FPhipMeasureSLHDsUDesigns_IUDesigns_IIUDesigns_III
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Find Best Model | Best_Model |
Measure of Discrete Discrepancy | Discrete_Discrepancy |
Latin Hypercube Designs (LHDs) for Prime Numbers | LHDs_I |
Latin Hypercube Designs (LHDs) for Any Numbers of Factors | LHDs_II |
Maximum Absolute Correlation | MAC |
Measure of Maxpro criterion | Maxpro_Measure |
Maximum Coincidence (or Meeting) numbers between rows | Meeting_Number |
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 |