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poissonreg

poissonreg enables the parsnip package to fit various types of Poisson regression models including ordinary generalized linear models, simple Bayesian models (via rstanarm), and two zero-inflated Poisson models (via pscl).

For agents: poissonreg R package reference for fitting Poisson regression models in parsnip, including Bayesian and zero-inflated variants; function docs such as tidy().

poisson regressionr packageparsniptidymodelszero-inflated modelsstatistical modeling

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# poissonreg

poissonreg enables the parsnip package to fit various types of Poisson
regression models including ordinary generalized linear models, simple
Bayesian models (via rstanarm), and two zero-inflated Poisson models
(via pscl).

## Installation

You can install the released version of poissonreg from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("poissonreg")
```

Install the development version from GitHub with:

``` r
# install.packages("pak")
pak::pak("tidymodels/poissonreg")
```

## Available Engines

The poissonreg package provides engines for the models in the following
table.

| model       | engine   | mode       |
|:------------|:---------|:-----------|
| poisson_reg | glm      | regression |
| poisson_reg | hurdle   | regression |
| poisson_reg | zeroinfl | regression |
| poisson_reg | glmnet   | regression |
| poisson_reg | stan     | regression |

## Example

A log-linear model for categorical data analysis:

``` r
library(poissonreg)

# 3D contingency table from Agresti (2007):
poisson_reg() |>
  set_engine("glm") |>
  fit(count ~ (.)^2, data = seniors)
#> parsnip model object
#> 
#> 
#> Call:  stats::glm(formula = count ~ (.)^2, family = stats::poisson, 
#>     data = data)
#> 
#> Coefficients:
#>               (Intercept)               marijuanayes  
#>                    5.6334                    -5.3090  
#>              cigaretteyes                 alcoholyes  
#>                   -1.8867                     0.4877  
#> marijuanayes:cigaretteyes    marijuanayes:alcoholyes  
#>                    2.8479                     2.9860
…

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