RoBTT: 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, <doi:10.3758/s13423-024-02590-5>). 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, <doi:10.31234/osf.io/j9f3s>). 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.

Package details

AuthorFrantišek Bartoš [aut, cre] (<https://orcid.org/0000-0002-0018-5573>), Maximilian Maier [aut] (<https://orcid.org/0000-0002-9873-6096>), Henrik R Godmann [aut] (<https://orcid.org/0009-0003-6410-4765>)
MaintainerFrantišek Bartoš <f.bartos96@gmail.com>
LicenseGPL-3
Version1.3.1
URL https://fbartos.github.io/RoBTT/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("RoBTT")

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RoBTT documentation built on April 12, 2025, 2:32 a.m.