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mmibain

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.

Installation

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")

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). 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()

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:

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