https://github.com/bertcarnell/lhs
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples
https://github.com/bertcarnell/lhs
latin-hypercube latin-hypercube-sample latin-hypercube-sampling lhs orthogonal-arrays r r-package
Last synced: about 2 months ago
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Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples
- Host: GitHub
- URL: https://github.com/bertcarnell/lhs
- Owner: bertcarnell
- License: gpl-3.0
- Created: 2018-12-01T20:59:29.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-30T23:16:19.000Z (11 months ago)
- Last Synced: 2025-03-30T11:07:24.507Z (2 months ago)
- Topics: latin-hypercube, latin-hypercube-sample, latin-hypercube-sampling, lhs, orthogonal-arrays, r, r-package
- Language: C++
- Homepage: https://bertcarnell.github.io/lhs/
- Size: 27 MB
- Stars: 45
- Watchers: 2
- Forks: 8
- Open Issues: 9
-
Metadata Files:
- Readme: README.Rmd
- Changelog: ChangeLog
- License: LICENSE.md
- Security: SECURITY.md
Awesome Lists containing this project
README
---
title: "lhs"
output:
md_document:
variant: markdown_github
---
```{r setup, include=FALSE}
require(knitr)
```
```{r badges, echo=FALSE, results="asis"}
cat(paste0("|",
paste0(
c("Actions", "Code Coverage", "Website", "Doxygen", "CRAN Downloads", "CRAN"),
collapse="|"),
"|\n"))
cat(paste0("|", paste0(rep(":---:", 6), collapse="|"), "|\n"))
cat(paste0("|",
paste0(c("[](https://github.com/bertcarnell/lhs/actions)",
"[](https://codecov.io/github/bertcarnell/lhs?branch=master)",
"[](https://bertcarnell.github.io/lhs/)",
"[](https://bertcarnell.github.io/lhs/html/index.html)",
"[](https://cran.r-project.org/package=lhs)",
"[](https://cran.r-project.org/package=lhs)"),
collapse="|"),
"|"))
```
# lhs
`lhs` provides a number of methods for creating and augmenting
Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
- Reverse Dependency Checks
- [Depends](etc/revdep_README_Depends.md)
- [Imports](etc/revdep_README_Imports.md)
- [Suggests](etc/revdep_README_Suggests.md)
- Docker Images for Testing
- [lhs-debug](https://hub.docker.com/repository/docker/bertcarnell/lhs-debug)
- [lhs-revdep](https://hub.docker.com/repository/docker/bertcarnell/lhs_revdep) built from [here](https://github.com/bertcarnell/r-debug)
## Installation
You can install the released version of `lhs` from [CRAN](https://CRAN.R-project.org) with:
```{r install1, echo=TRUE, eval=FALSE}
install.packages("lhs")
```
You can also install the development version of `lhs` from github with:
```{r install2, echo=TRUE, eval=FALSE}
if (!require(devtools)) install.packages("devtools")
devtools::install_github("bertcarnell/lhs")
```
## Quick Start
Create a random LHS with 10 samples and 3 variables:
```{r random, echo=TRUE}
require(lhs)
set.seed(1776)
X <- randomLHS(n = 10, k = 3)
```
Create a design that is more optimal than the random case:
```{r compare, echo=TRUE}
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)
```
```{r compare.table, echo=FALSE}
df1 <- data.frame(
method = c("random","genetic","maximin","maximin","improved","optimum"),
mean_dist = c(mean(dist(X)), mean(dist(X_gen)), mean(dist(X_max1)),
mean(dist(X_max2)), mean(dist(X_imp)), mean(dist(X_opt))),
min_dist = c(min(dist(X)), min(dist(X_gen)), min(dist(X_max1)),
min(dist(X_max2)), min(dist(X_imp)), min(dist(X_opt))))
knitr::kable(df1[order(df1$min_dist, decreasing = TRUE),],
col.names = c("Method", "Mean Distance", "Minimum Distance"),
digits = 4)
```
Augment an existing design:
```{r augment, echo=TRUE}
Y <- randomLHS(10, 5)
Z <- augmentLHS(Y, 2)
dim(Z)
```
Build an orthogonal array LHS:
```{r oalhs, echo=TRUE}
# 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)
# 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)
```
## Help
R-Help Examples of using the LHS package
- [Latin hyper cube sampling from expand.grid()](https://stat.ethz.ch/pipermail/r-help/2007-January/124143.html)
- [Latin Hypercube Sampling with a condition](https://stat.ethz.ch/pipermail/r-help/2011-June/279906.html)
- [Latin Hypercube with condition sum = 1](https://stat.ethz.ch/pipermail/r-help/2008-November/180929.html)
- [Latin hypercube sampling](https://www.mail-archive.com/[email protected]/msg192704.html)
- [Latin Hypercube Sample and transformation to uniformly distributed integers or classes](https://stat.ethz.ch/pipermail/r-help/2013-October/361263.html)
- [Latin hypercube sampling from a non-uniform distribution](https://stat.ethz.ch/pipermail/r-help/2017-August/448475.html)
- [Latin Hypercube Sampling when parameters are defined according to specific probability distributions](https://stat.ethz.ch/pipermail/r-help/2017-June/447266.html)
StackExchange Examples:
- [Latin Hypercube around set points](https://stats.stackexchange.com/questions/370983/latin-hypercube-around-set-points)
- [Latin hypercube sampling with categorical variables](https://stats.stackexchange.com/questions/388963/latin-hypercube-sampling-with-categorical-variables)
- [Are Latin hypercube samples uncorrelated](https://stats.stackexchange.com/questions/147789/are-latin-hypercube-samples-uncorrelated)
- [Stopping rule for Latin hypercube sampling (LHS)](https://stats.stackexchange.com/questions/407262/stopping-rule-for-latin-hypercube-sampling-lhs)
- [Is a group of random hypercube samples equivalent to a single latin hypercube with more samples?](https://stats.stackexchange.com/questions/411085/is-a-group-of-random-hypercube-samples-equivalent-to-a-single-latin-hypercube-wi)
- [Taking samples of data using Latin Hypercube Sampling](https://stats.stackexchange.com/questions/439271/taking-samples-of-data-using-latin-hypercube-sampling)
- [Number of parameter sets generated by latin hyercube sampling](https://stats.stackexchange.com/questions/460918/number-of-parameter-sets-generated-by-latin-hyercube-sampling)
- [Is there a way to check if sample obeys the Latin Hypercube Sampling rule?](https://stats.stackexchange.com/questions/465492/is-there-a-way-to-check-if-sample-obeys-the-latin-hypercube-sampling-rule)
- [Effectiveness of Latin Hypercube Sampling](https://stats.stackexchange.com/questions/468202/effectiveness-of-latin-hypercube-sampling)
- [Dividing CDF rather than PDF equally in Latin Hypercube Sampling](https://stats.stackexchange.com/questions/468293/dividing-cdf-rather-than-pdf-equally-in-latin-hypercube-sampling)
- [Stratified sampling / QMC simulation for compound Poisson rv](https://stats.stackexchange.com/questions/469963/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](https://stats.stackexchange.com/questions/474911/using-latin-hypercube-sampling-with-a-condition-that-the-sum-of-two-variables-sh)
- [How to generate a design for a response surface with a discrete input random variable?](https://stats.stackexchange.com/questions/444997/how-to-generate-a-design-for-a-response-surface-with-a-discrete-input-random-var)
- [Is it necessary to shuffle X coordinates in Latin hypercube Sampling?](https://stats.stackexchange.com/questions/498492/is-it-necessary-to-shuffle-x-coordinates-in-latin-hypercube-sampling-lhc)
## Other
lhs package announcement: [R-pkgs New R-Packages: Triangle and LHS](https://stat.ethz.ch/pipermail/r-packages/2006/000715.html)