Does a metropolis hastings for the Erlang distribution

1 2 | ```
mcmc.erlang(dat, prior.par1, prior.par2, init.pars,
verbose, burnin, n.samples, sds = c(1, 1))
``` |

`dat` |
the data to fit |

`prior.par1` |
mean of priors. A negative binomial (for shape) and a normal for log(scale) |

`prior.par2` |
dispersion parameters for priors, dispersion for negative binomial, log scale sd for normal |

`init.pars` |
the starting parameters on the reporting scale |

`verbose` |
how often to print an update |

`burnin` |
how many burnin iterations to do |

`n.samples` |
the number of samples to keep and report back |

`sds` |
the standard deviations for the proposal distribution |

a matrix of n.samples X 2 parameters, on the estimation scale

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