This package provides an implementation of Bayesian model-averaged t-test 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 pre-print Maier et al. (2022) introducing the method.
We also prepared a vignette that illustrate functionality of the package:
The release version can be installed from CRAN:
and the development version of the package can be installed from GitHub:
Maier, M., Bartoš, F., Quintana, D. S., Bergh, D. van den, Marsman, M., Ly, A., & Wagenmakers, E.-J. (2022). Model-averaged Bayesian t-tests. https://doi.org/10.31234/osf.io/d5zwc