Bindings for Bayesian TidyModels

Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) < https://www.tidymodels.org>). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) , Bürkner (2018) , and Carpenter et al. (2017) .

Bindings for Bayesian TidyModels

bayesian supports Bayesian modeling using brms/Stan with parsnip/tidymodels.

Installation

The stable version of bayesian can be installed from CRAN using:

install.packages("bayesian")

The development version of bayesian can be installed from GitHub using:

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

Example

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.

Citation

To cite bayesian in publications, please use:

citation("bayesian")

Hamada S. Badr and Paul C. Bürkner (2024): bayesian: Bindings for Bayesian TidyModels, Comprehensive R Archive Network (CRAN). URL: https://hsbadr.github.io/bayesian/.

Contributing

This project is released with a Contributor Code of Conduct. 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.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.