Robust Bayesian T-Test
Check fitted 'RoBTT' object for errors and warnings
Prints summary of "RoBTT" ensemble implied by the specified priors
Checks a fitted RoBTT object
Interprets results of a 'RoBTT' model.
Reports whether x is a 'RoBTT' object
Plots a fitted 'RoBTT' object
Prints a fitted 'RoBTT' object
Prints summary object for 'RoBTT' method
Creates a prior distribution
Creates a prior distribution
rho to log standard deviation ratio transformations
Convergence checks of the fitting process
Options for the 'RoBTT' package
RoBTT: Robust Bayesian t-test
Estimate a Robust Bayesian T-Test
Summarize fitted 'RoBTT' object
Updates a fitted RoBTT object
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.