The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.