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`lhs`

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

- Reverse Dependency Checks
- Docker Images for Testing
- lhs-debug
- lhs-revdep built from here

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")
```

Create a random LHS with 10 samples and 3 variables:

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)
```

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)
```

```
# 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)
```

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?

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