Performing Bayesian Inference for Repeated-Measures Designs R package rmBayes version 0.1.16 A Bayesian credible interval is interpreted with respect to posterior probability, and this interpretation is far more intuitive than that of a frequentist confidence interval. However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subject effects, rendering its relationship with the Bayes factor less clear in within-subject (repeated-measures) designs. This urgent issue can be addressed by using within-subject intervals in within-subject designs, which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, the Loftus-Masson (1994) <doi:10.3758/BF03210951>, the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates. Software Depends: R (>= 3.5.0) Imports: methods, Rcpp (>= 0.12.0), RcppParallel, rstan (>= 2.26.0), rstantools (>= 2.1.1), stats LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel, rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: knitr, testthat, rmarkdown, covr Zhengxiao Wei <zhengxiao@uvic.ca> Comprehensive R Archive Network (CRAN) Zhengxiao Wei [aut, cre] (ORCID: <https://orcid.org/0000-0003-1866-2320>), Farouk S. Nathoo [aut] (ORCID: <https://orcid.org/0000-0002-2569-3507>), Michael E. J. Masson [aut] (ORCID: <https://orcid.org/0000-0002-5430-6078>) GPL (>= 3) 2024-02-19 application/tgz https://CRAN.R-project.org/package=rmBayes doi:10.32614/CRAN.package.rmBayes