<table> <tr> <td> <img align="left" width="200" height="200" src="logo.svg"/> </td> </tr> </table>

| _Actions | _{Code Coverage} | _Website | _Doxygen | _{CRAN Downloads} | _CRAN | |:----------:|:----------:|:----------:|:----------:|:----------:|:----------:| | | | | | | |

lhs

lhs provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.

• Reverse Dependency Checks
  • Depends
  • Imports
  • Suggests
• Docker Images for Testing
  • lhs-debug
  • lhs-revdep built from here

Installation

You can install the released version of lhs from CRAN with:

install.packages("lhs")

You can also install the development version of lhs from github with:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("bertcarnell/lhs")

Quick Start

Create a random LHS with 10 samples and 3 variables:

require(lhs)

## Loading required package: lhs

set.seed(1776)
X <- randomLHS(n = 10, k = 3)

Create a design that is more optimal than the random case:

X_gen <- geneticLHS(10, 3, pop = 100, gen = 5, pMut = 0.1)
X_max1 <- maximinLHS(10, 3, method = "build", dup = 5)
X_max2 <- maximinLHS(10, 3, method = "iterative", optimize.on = "result", eps = 0.01, maxIter = 300)
X_imp <- improvedLHS(10, 3, dup = 5)
X_opt <- optimumLHS(10, 3, maxSweeps = 10, eps = 0.01)

| | Method | Mean Distance | Minimum Distance | |:----|:---------|--------------:|-----------------:| | 6 | optimum | 0.7289 | 0.4598 | | 2 | genetic | 0.7190 | 0.4059 | | 4 | maximin | 0.7246 | 0.3975 | | 5 | improved | 0.7028 | 0.3872 | | 3 | maximin | 0.7296 | 0.3611 | | 1 | random | 0.7067 | 0.2709 |

Augment an existing design:

Y <- randomLHS(10, 5)
Z <- augmentLHS(Y, 2)
dim(Z)

## [1] 12  5

Build an orthogonal array LHS:

# a 9 row design is returned because a 10 row design is not possible with these algorithms
W9 <- create_oalhs(10, 3, bChooseLargerDesign = FALSE, bverbose = FALSE)
dim(W9)

## [1] 9 3

# a 16 row design is returned because a 10 row design is not possible with these algorithms
W16 <- create_oalhs(10, 3, bChooseLargerDesign = TRUE, bverbose = FALSE)
dim(W16)

## [1] 16  3

Help

R-Help Examples of using the LHS package

• Latin hyper cube sampling from expand.grid()
• Latin Hypercube Sampling with a condition
• Latin Hypercube with condition sum = 1
• Latin hypercube sampling
• Latin Hypercube Sample and transformation to uniformly distributed integers or classes
• Latin hypercube sampling from a non-uniform distribution
• Latin Hypercube Sampling when parameters are defined according to specific probability distributions

StackExchange Examples:

• Latin Hypercube around set points
• Latin hypercube sampling with categorical variables
• Are Latin hypercube samples uncorrelated
• Stopping rule for Latin hypercube sampling (LHS)
• Is a group of random hypercube samples equivalent to a single latin hypercube with more samples?
• Taking samples of data using Latin Hypercube Sampling
• Number of parameter sets generated by latin hyercube sampling
• Is there a way to check if sample obeys the Latin Hypercube Sampling rule?
• Effectiveness of Latin Hypercube Sampling
• Dividing CDF rather than PDF equally in Latin Hypercube Sampling
• Stratified sampling / QMC simulation for compound Poisson rv
• Using Latin Hypercube Sampling with a condition that the sum of two variables should be less than one
• How to generate a design for a response surface with a discrete input random variable?
• Is it necessary to shuffle X coordinates in Latin hypercube Sampling?

Other

lhs package announcement: R-pkgs New R-Packages: Triangle and LHS