https://github.com/cjvanlissa/bain

Bayes Factors for Informative Hypotheses

https://github.com/cjvanlissa/bain

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Bayes Factors for Informative Hypotheses

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```


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# 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).

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