optimumLHS(n=10, k=2, maxSweeps=2, eps=.1)n by k Latin Hypercube Sample matrix with values uniformly distributed on [0,1]k variables, the range of each variable is divided
into n equally probable intervals. n sample points are then drawn such that a
Latin Hypercube is created. Latin Hypercube sampling generates more efficient
estimates of desired parameters than simple Monte Carlo sampling.
This program generates a Latin Hypercube Sample by creating random permutations
of the first n integers in each of k columns and then transforming those
integers into n sections of a standard uniform distribution. Random values are
then sampled from within each of the n sections. Once the sample is generated,
the uniform sample from a column can be transformed to any distribution by
using the quantile functions, e.g. qnorm(). Different columns can have
different distributions.
S-optimality seeks to maximize the mean distance from each design point to all
the other points in the design, so the points are as spread out as possible.
This function uses the CP algorithm to generate an optimal
design with respect to the S optimality criterion.randomLHS, geneticLHS,
improvedLHS and maximinLHS
to generate Latin Hypercube Samples.
optAugmentLHS, optSeededLHS, and
augmentLHS to modify and augment existing designs.