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---
title: "README"
bibliography: inst/REFERENCES.bib
csl: inst/apa.csl
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r include = FALSE, eval = FALSE}
```
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
dev = "png"
)
if(.Platform$OS.type == "windows"){
knitr::opts_chunk$set(dev.args = list(type = "cairo"))
}
```
<!-- badges: start -->
[](https://CRAN.R-project.org/package=RoBTT)
<!-- badges: end -->
<!--
#[](https://github.com/FBartos/RoBTT/actions)
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#[](https://app.codecov.io/gh/FBartos/RoBTT?branch=master)
-->
# Robust Bayesian T-Test (RoBTT)
This package provides an implementation of Bayesian model-averaged t-tests that allows users to draw inference about the presence vs absence of the effect, heterogeneity of variances, and outliers. The RoBTT packages estimates model ensembles of models created as a combination of the competing hypotheses and uses Bayesian model-averaging to combine the models using posterior model probabilities. Users can obtain the model-averaged posterior distributions and inclusion Bayes factors which account for the uncertainty in the data generating process. User can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, and fit diagnostics.
See our manuscripts for more information about the methodology:
- @maier2022bayesian introduces a robust Bayesian t-test that model-averages over normal and t-distributions to account for the uncertainty about potential outliers,
- @godmann2024how introduces a truncated Bayesian t-test that accounts for outlier exclusion when estimating the models.
We also prepared vignettes that illustrate functionality of the package:
- [Introduction to RoBTT](https://fbartos.github.io/RoBTT/articles/Introduction_to_RoBTT.html)
- [Truncated T-Tests](https://fbartos.github.io/RoBTT/articles/Truncated_t_test.html)
## Installation
The release version can be installed from CRAN:
``` r
install.packages("RoBTT")
```
and the development version of the package can be installed from GitHub:
``` r
devtools::install_github("FBartos/RoBTT")
```
### References