Robust Bayesian T-Test

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, ). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, ). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

README

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:

• Maier et al. (2024) introduces a robust Bayesian t-test that model-averages over normal and t-distributions to account for the uncertainty about potential outliers,
• Godmann et al. (2024) 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
• Truncated T-Tests

Installation

The release version can be installed from CRAN:

install.packages("RoBTT")

and the development version of the package can be installed from GitHub:

devtools::install_github("FBartos/RoBTT")

References