Simulate N- replicates from the predictive distribution for a given time point (tpt) from 1 to n (length of the data).

1 | ```
pred.dist.simul(hyperest, tpt, include.obs = T, N = 1)
``` |

`hyperest` |
Output from the |

`tpt` |
A specific time point - from 1 to n corresponding to the number of time points from the data set |

`include.obs` |
If TRUE, the observed data for time |

`N` |
Number of replicates |

A matrix with N rows; the number of columns depends on whether the observed data are returned

The columns are organized consistent with the observed data
(ie. *u\times p* ungauged blocks, *g1\times p*,
*g_2\times p*, ...)

This function could be slow if there are missing data at gauged sites correspondind to the selected time point. That is, it is fastest at time points corresponding to Block 1 and slower with higher blocks.

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

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.