Computer Experiment Designs
Find Best Model
Measure of Discrete Discrepancy
Latin Hypercube Designs (LHDs) for Prime Numbers
Latin Hypercube Designs (LHDs) for Any Numbers of Factors
Maximum Absolute Correlation
Measure of Maxpro criterion
Maximum Coincidence (or Meeting) numbers between rows
Nearly Orthogonal Latin Hypercube Designs for Flexible Levels and Fact...
Two Factor Orthogonal Latin Hypercube Designs
Phi_p criterion
Sliced Latin Hypercube Designs with Equal Size of Slices
Orthogonal Uniform Designs with Two Factors
Uniform Designs with Multiple Factors with Minimal Runs
Nearly Orthogonal Uniform Designs for Two and Four Factors
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.