Does a metropolis hastings for the Erlang distribution

Usage

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