RoBTT: Robust Bayesian T-Test

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 (Maier et al., 2022, <doi:10.31234/osf.io/d5zwc>). Users can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, 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
Version1.3.0
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 May 29, 2024, 12:03 p.m.