README.Rmd

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# mmibain

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The Mighty Metrika Interface to BAIN ('mmibain') R package provides Shiny apps to explore basic functionality of the ['bain'](https://informative-hypotheses.sites.uu.nl/software/bain/) package for BAyesian INformative Hypotheses Evaluation.

## Installation

You can install the released version of 'mmibain' from [CRAN](https://CRAN.R-project.org):

```{r eval=FALSE}
install.packages("mmibain")
```

You can install the development version of 'mmibain' from [GitHub](https://github.com/) with:

```{r eval=FALSE}
# install.packages("devtools")
devtools::install_github("mightymetrika/mmibain")
```


## Play RepliCrisis

'RepliCrisis' is a Shiny app game that simulates evalutating replication studies based on the framework presented in [Hoijtink, Mulder, van Lissa & Gu (2019)](https://doi.org/10.1037/met0000201). Follow these steps to play:

* Set your sample size (for groups within study), difficulty, alpha level, and seed for reproducibility.
* Define thresholds for the Bayes Factor and Posterior Model Probability to assess evidence in favor of the original study.
* Conduct the original study to generate data and form a hypothesis.
* Show diagnostics and descriptives to understand statistical results and hypotheses.
* Conduct a replication study, using swap controls to match the original study's results.
* Run replication analysis to evaluate the results against the original hypothesis.
* Start a new game by conducting a new original study.

To play, load 'mmibain' and call the RepliCrisis() function:

```{r eval=FALSE}
library(mmibain)
RepliCrisis()
```


## mmibain Shiny App

The package also includes a Shiny app for running basic bain::bain() models:

* Upload your data in CSV format.
* Choose your modeling engine (lm, t_test, lavaan).
* Input your model and any additional arguments.
* Fit the model and input hypotheses for evaluation.
* Adjust settings such as the fraction parameter, standardized regression coefficients, and confidence intervals.
* Set a seed for reproducible results.
* Run the Bayesian Informative Hypotheses Evaluation.

Launch the app with:

```{r eval=FALSE}
mmibain()
```


# References
Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological methods, 24(5), 539–556. https://doi.org/10.1037/met0000201