# Optimum Seeded Latin Hypercube Sample

### Description

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function then uses the
columnwise pairwise (CP) algoritm to optimize the design. The original design
is not necessarily maintained.

### Usage

1 | ```
optSeededLHS(seed, m=1, maxSweeps=2, eps=.1, verbose=FALSE)
``` |

### Arguments

`seed` |
The number of partitions (simulations or design points) |

`m` |
The number of additional points to add to matrix |

`maxSweeps` |
The maximum number of times the CP algorithm is applied to all the columns. |

`eps` |
The optimal stopping criterion |

`verbose` |
Print informational messages |

### Details

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function then uses the
CP algoritm to optimize the design. The original design
is not necessarily maintained.

### Value

An `n`

by `k`

Latin Hypercube Sample matrix with values uniformly distributed on [0,1]

### Author(s)

Rob Carnell

### References

Stein, M. (1987)
Large Sample Properties of Simulations Using Latin Hypercube Sampling.
*Technometrics*.
**29**, 143–151.

### See Also

`randomLHS`

, `geneticLHS`

,
`improvedLHS`

, `maximinLHS`

, and
`optimumLHS`

to generate Latin Hypercube Samples.
`optAugmentLHS`

and
`augmentLHS`

to modify and augment existing designs.

### Examples

1 2 3 | ```
a <- randomLHS(4,3)
a
optSeededLHS(a, 2, 2, .1)
``` |