In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("poisson_reg", "glm")

Tuning Parameters

This engine has no tuning parameters.

Translation from parsnip to the underlying model call (regression)

r uses_extension("poisson_reg", "glm", "regression")

library(poissonreg)

poisson_reg() %>%
  set_engine("glm") %>%
  translate()

Preprocessing requirements

Case weights

However, the documentation in [stats::glm()] assumes that is specific type of case weights are being used:"Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations. For a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes: they would rarely be used for a Poisson GLM."

If frequency weights are being used in your application, the [glm_grouped()] model (and corresponding engine) may be more appropriate.