https://github.com/mightymetrika/mmibain
Bayesian Informative Hypotheses Evaluation Web Applications
https://github.com/mightymetrika/mmibain
bayes-factor bayesian hypothesis informative r statistics
Last synced: 11 months ago
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Bayesian Informative Hypotheses Evaluation Web Applications
- Host: GitHub
- URL: https://github.com/mightymetrika/mmibain
- Owner: mightymetrika
- License: other
- Created: 2023-10-07T14:12:50.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-17T02:16:20.000Z (about 2 years ago)
- Last Synced: 2024-05-23T03:04:33.845Z (about 2 years ago)
- Topics: bayes-factor, bayesian, hypothesis, informative, r, statistics
- Language: R
- Homepage:
- Size: 4.61 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
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README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mmibain
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