gMLE.pg: Negative-Binomial Parameter Estimation
In BayesGOF: Bayesian Modeling via Frequentist Goodness-of-Fit

Description Usage Arguments Value Author(s) References Examples

Computes Type-II Maximum likelihood estimates \hat{α} and \hat{β} for gamma prior g\sim Gamma(α, β).

1	gMLE.pg(cnt.vec, exposure = NULL, start.par = c(1,1))

`cnt.vec`	Vector containing Poisson counts.
`exposure`	Vector containing exposures for each count. The default is no exposure, thus `exposure = NULL`.
`start.par`	Initial values that will pass to `optim`.

Returns a vector where the first component is α and the second component is the scale parameter β for the gamma distribution: \frac{1}{Γ(α)β^α} θ^{α-1}e^{-\frac{θ}{β}}.

Doug Fletcher

Koenker, R. and Gu, J., 2017. "REBayes: An R Package for Empirical Bayes Mixture Methods," Journal of Statistical Software, Articles, 82(8), pp. 1-26.

### without exposure
data(ChildIll)
ill.start <- gMLE.pg(ChildIll)
ill.start
### with exposure
data(NorbergIns)
X <- NorbergIns$deaths
E <- NorbergIns$exposure/344
norb.start <- gMLE.pg(X, exposure = E)
norb.start