The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
bayesian supports Bayesian modeling using brms/Stan with parsnip/tidymodels.
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")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.
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/.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.