Poisson with an autoregressive term I want to fit a fairly "standard" Poisson model, but with an autoregressive term.
$N_i \sim \mathrm{Pois}( \lambda_i E_i)$
with $\log \lambda_i = X_i \beta + \delta$
$\delta \sim AR(1)$
$X_i$ is a vector of covariates.
$\beta$ are my coefficients.
$\delta$ is an autoregressive term.
$E_i$ is the size of population at time t. 
The idea is that the count at time step $t$ is partially dependent on the count at time step $t-1$.
Ideally, I'd like to find some R package to fit this.
Any suggestions?
 A: I think you are looking for the model in Brandt et al. (2000) there called PEWMA, after the forecast function.  R code to fit it is available here.  
The paper also has some general discussion of possible conditionally Poisson AR models.  Fro more of that, chapter 7 of Cameron and Trivedi (1998) is useful.
A: Have you considered a Transfer Function between N and E and your other covariates which could encode changes in parameters over time , changes in error variance over time , any necessaery autoregressive structure possibly proxying unspecified seasonal drivers and other omitted structure like level shifts, seasonal pulses and local time trends. Accomplished without letting "unusual values" distort the model/parameters. I have seen approaches like this referred to in the literature as DARIMA MODELS where the D stands for discrete. Various forms of Power Transforms such as Logs might be needed to possibly decouple the expected value from the variance of the errors ( always a good idea when needed! ) These kinds of models can be useful in detecting the possible impact of changes in N as it relates to the prediction of E.
