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https://github.com/cjvanlissa/bain

Bayes Factors for Informative Hypotheses
https://github.com/cjvanlissa/bain

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Bayes Factors for Informative Hypotheses

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

[![CRAN status](https://www.r-pkg.org/badges/version/bain)](https://cran.r-project.org/package=bain)

[![R-CMD-check](https://github.com/cjvanlissa/bain/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/cjvanlissa/bain/actions/workflows/R-CMD-check.yaml)

[![](https://cranlogs.r-pkg.org/badges/bain)](https://cran.r-project.org/package=bain)

[![test-coverage](https://github.com/cjvanlissa/bain/workflows/test-coverage/badge.svg)](https://github.com/cjvanlissa/bain/actions)

[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html)

[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/3871/badge)](https://bestpractices.coreinfrastructure.org/projects/3871)



# bain



Bain stands for Bayesian informative hypothesis evaluation. It computes Bayes factors for informative hypotheses in a wide variety of statistical models. Just run your analysis as usual, and then apply bain to the output. A tutorial is available at [DOI:10.1037/met0000201](https://doi.org/10.1037/met0000201). A sequel with the focus on Structural Equation Models is available at https://doi.org/10.1080/10705511.2020.1745644.



## Installation



Install the latest release version of `bain` from CRAN:



```{r cran-installation, eval = FALSE}

install.packages("bain")

```



You can also install the latest development version of `bain` from GitHub. This requires a working toolchain, to compile the Fortran source code. [Step 3 in this tutorial](https://cjvanlissa.github.io/worcs/articles/setup.html) explains how to set up the toolchain. Then, run:



```{r github-installation, eval = FALSE}

install.packages("devtools")

devtools::install_github("cjvanlissa/bain")

```



## Workflow



Add bain to your existing R workflow, and obtain Bayes factors for your familiar R analyses! Bain is compatible with the pipe operator. Here is an example for testing an informative hypothesis about mean differences in an ANOVA:



```{r example, eval = TRUE, message = FALSE}

# Load bain

library(bain)

# dplyr to access the %>% operator

library(dplyr)

# Iris as example data

iris %>%

  # Select outcome and predictor variables

  select(Sepal.Length, Species) %>%      

  # Add -1 to the formula to estimate group means, as in ANOVA

  lm(Sepal.Length ~ -1 + Species, .) %>% 

  bain("Speciessetosa < Speciesversicolor = Speciesvirginica;

       Speciessetosa < Speciesversicolor < Speciesvirginica")

```



## Documentation



Every user-facing function in the package is documented, and the documentation can be accessed by running `?function_name` in the R console, e.g., `?bain`.



Moreover, you can read the *Introduction to bain* vignette by running `vignette("Introduction_to_bain", package = "bain")`



## Citing bain



You can cite the R-package with the following citation:



> Gu, X., Hoijtink, H., Mulder, J., & van Lissa, C. (2019). bain: Bayes factors for informative hypotheses. (Version 0.2.3) [R package]. https://CRAN.R-project.org/package=bain



## Contributing and Contact Information



If you have ideas, please get involved. You can contribute by opening an issue on GitHub, or sending a pull request with proposed features. Contributions in code must adhere to the [tidyverse style guide](https://style.tidyverse.org/).



* File a GitHub issue [here](https://github.com/cjvanlissa/bain)

* Make a pull request [here](https://github.com/cjvanlissa/bain/pulls)



By participating in this project, you agree to abide by the [Contributor Code of Conduct v2.0](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html).