The Mighty Metrika Interface to BAIN (‘mmibain’) R package provides Shiny apps to explore basic functionality of the ‘bain’ package for BAyesian INformative Hypotheses Evaluation.
You can install the released version of ‘mmibain’ from CRAN:
install.packages("mmibain")You can install the development version of ‘mmibain’ from GitHub with:
# install.packages("devtools")
devtools::install_github("mightymetrika/mmibain")‘RepliCrisis’ is a Shiny app game that simulates evalutating replication studies based on the framework presented in Hoijtink, Mulder, van Lissa & Gu (2019). 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:
library(mmibain)
RepliCrisis()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:
mmibain()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