---
title: "Introduction"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r global-options, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE
)
```

```{r setup, echo = FALSE}
library(Rprobit)
```

**Rprobit** is an R package for the estimation of multinomial probit models.

## Outline of the Vignettes

1. [Data and Simulation](data_simulation.html)
2. [Model Definition](model_definition.html)
3. [Modeling Setup](modeling_setup.html)
4. [Model Fitting](model_fitting.html)

## License

**Rprobit** is licensed under the GNU General Public License v3.0.

## Contact

Maintainer: [Dietmar Bauer](mailto:dietmar.bauer@uni-bielefeld.de)

Have you found a bug or want to request a feature? [Please tell us!](mailto:dietmar.bauer@uni-bielefeld.de)

## Installing Rprobit

To install the latest version of **Rprobit**, download the source package file from CRAN and run `install.packages("Rprobit")` in your R console.

## Typical Workflow

The estimation of multinomial probit models follows a typical workflow:

1. Prepare data sets: **Rprobit** can handle data sets in *wide* and in *long* format. See [Data and Simulation](data_simulation.html).
2. Define the model to be estimated. The model formula and the model parameterization need to be specified. See [Model Definition](model_definition.html).
3. Set up the `Rprobit` object, which contains the model formulation and the data set. See [Modeling Setup](modeling_setup.html).
4. Fit the model. See [Model Fitting](model_fitting.html).
5. Use the model for prediction. See [Model Fitting](model_fitting.html).