README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# Bindings for Bayesian TidyModels [<img src="man/figures/bayesian.png" align="right" width="160" alt="bayesian logo" />](https://hsbadr.github.io/bayesian/)

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[**`bayesian`**](https://hsbadr.github.io/bayesian/) supports Bayesian modeling using [`brms`](https://paul-buerkner.github.io/brms/)/[`Stan`](https://mc-stan.org/)
with [`parsnip`](https://parsnip.tidymodels.org/)/[`tidymodels`](https://www.tidymodels.org/).

## Installation

The stable version of [`bayesian`](https://hsbadr.github.io/bayesian/) can be installed from [CRAN](https://CRAN.R-project.org/package=bayesian) using:

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

The development version of [`bayesian`](https://hsbadr.github.io/bayesian/) can be installed from [GitHub](https://github.com/hsbadr/bayesian) using:

``` r
install.packages("pak")
pak::pkg_install("hsbadr/bayesian")
```

## Example


``` r
library(bayesian)

bayesian_mod <-
  bayesian() |>
  set_engine("brms") |>
  fit(
    rating ~ treat + period + carry + (1 | subject),
    data = inhaler
  )

summary(bayesian_mod$fit)
```

For more details, [get started with `bayesian`](https://hsbadr.github.io/bayesian/articles/GetStarted.html).

## Citation

To cite `bayesian` in publications, please use:

```r
citation("bayesian")
```

> Hamada S. Badr and Paul C. Bürkner (2024):
bayesian: Bindings for Bayesian TidyModels,
[_Comprehensive R Archive Network (CRAN)_](https://cran.r-project.org/package=bayesian).
URL: [https://hsbadr.github.io/bayesian/](https://hsbadr.github.io/bayesian/).

## Contributing

This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://forum.posit.co/).

- If you think you have encountered a bug, please [submit an issue](https://github.com/hsbadr/bayesian/issues).

- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org) (a minimal, reproducible example), to clearly communicate about your code.

- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).