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.
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:
We also prepared vignettes 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:
Godmann, H. R., Bartoš, F., & Wagenmakers, E.-J. (2024). A truncated t-test: Excluding outliers without biasing the Bayes factor.
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
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.