Transforms such that the transformed marginal distributions are correlated (as Margins and I hope to add that to my package in the future. It is possible to draw a Latin hypercube with correlated In my experience with Latin hypercube samples, most people draw the sample onĪ uniform hypercube and then transform the uniform cube to have newĭistributions on the margins. Is there a way to maintain the random LHS (with uniformlyĭistributed parameters) so that the refered condition is fulfilled? Answer If I divide each of these parameters with the sum, the uniform distribution is I want the combination of theįirst three parameters to sum up to 1 (which obviously do not) I am trying to do a Latin Hypercube Sampling (LHS) to a 5-parameterĭesign matrix. } else stop( "must have more than 1 intGroup")Ī <- min(intGroups]) + (i - 1) *(ranges] + spacing) /nī <- min(intGroups]) + i *(ranges] + spacing) /n - 1 if (a < b) runifint: Create a Random Sample of Uniform Integers.randomLHS: Construct a random Latin hypercube design.poly_sum: Addition in polynomial representation.poly_prod: Multiplication in polynomial representation.poly2int: Convert polynomial to integer in 0.q-1.optSeededLHS: Optimum Seeded Latin Hypercube Sample.optimumLHS: Optimum Latin Hypercube Sample.
![latin hypercube sampling normal distribution latin hypercube sampling normal distribution](https://image.slidesharecdn.com/lhs-170217020840/85/latin-hypercube-sampling-2-320.jpg)
optAugmentLHS: Optimal Augmented Latin Hypercube Sample.oa_to_oalhs: Create a Latin hypercube from an orthogonal array.maximinLHS: Maximin Latin Hypercube Sample.lhs-package: lhs: Latin Hypercube Samples.improvedLHS: Improved Latin Hypercube Sample.get_library_versions: Get version information for all libraries in the lhs package.geneticLHS: Latin Hypercube Sampling with a Genetic Algorithm.create_oalhs: Create an orthogonal array Latin hypercube.create_galois_field: Create a Galois field.createBusht: Create an orthogonal array using the Bush algorithm with.createBush: Create an orthogonal array using the Bush algorithm.createBoseBushl: Create an orthogonal array using the Bose-Bush algorithm with.createBoseBush: Create an orthogonal array using the Bose-Bush algorithm.
![latin hypercube sampling normal distribution latin hypercube sampling normal distribution](https://www.researchgate.net/profile/Ali-Omar-3/publication/236577544/figure/tbl2/AS:668422891397130@1536375844324/Ranges-of-the-Variables-Used-to-Generate-500-Random-Combinations-of-Inputs-for-T-Matrix.png)
createBose: Create an orthogonal array using the Bose algorithm.createAddelKempN: Create an orthogonal array using the Addelman-Kempthorne.createAddelKemp3: Create an orthogonal array using the Addelman-Kempthorne.
![latin hypercube sampling normal distribution latin hypercube sampling normal distribution](https://www.theinformationlab.co.uk/wp-content/uploads/2019/06/pane-21.png)