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.
Version: 1.3.1 Depends: R (≥ 4.0.0), Rcpp (≥ 0.12.19) Imports: rstan (≥ 2.26.0), rstantools (≥ 1.5.0), RcppParallel (≥ 5.0.1), BayesTools (≥ 0.2.15), bridgesampling, methods, ggplot2, Rdpack LinkingTo: StanHeaders (≥ 2.26.0), rstan (≥ 2.26.0), BH (≥ 1.69.0), Rcpp (≥ 0.12.15), RcppEigen (≥ 0.3.3.4.0), RcppParallel (≥ 5.0.1) Suggests: parallel, testthat, vdiffr, knitr, rmarkdown, covr Published: 2024-11-10 DOI: 10.32614/CRAN.package.RoBTT Author: FrantiÅ¡ek BartoÅ¡ [aut, cre], Maximilian Maier [aut], Henrik R Godmann [aut] Maintainer: FrantiÅ¡ek BartoÅ¡ <f.bartos96 at gmail.com> BugReports: https://github.com/FBartos/RoBTT/issues License: GPL-3 URL: https://fbartos.github.io/RoBTT/ NeedsCompilation: yes SystemRequirements: GNU make Citation: RoBTT citation info Materials: README NEWS CRAN checks: RoBTT results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=RoBTT to link to this page.
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