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.

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

`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 |

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.

An `n`

by `k`

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

Rob Carnell

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

`randomLHS`

, `geneticLHS`

,
`improvedLHS`

, `maximinLHS`

, and
`optimumLHS`

to generate Latin Hypercube Samples.
`optAugmentLHS`

and
`augmentLHS`

to modify and augment existing designs.

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

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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