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rmBayes: Performing Bayesian Inference for Repeated-Measures Designs

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.

Version: 0.1.16
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
Published: 2024-02-19
Author: Zhengxiao Wei ORCID iD [aut, cre], Farouk S. Nathoo ORCID iD [aut], Michael E. J. Masson ORCID iD [aut]
Maintainer: Zhengxiao Wei <zhengxiao at uvic.ca>
BugReports: https://github.com/zhengxiaoUVic/rmBayes/issues
License: GPL (≥ 3)
URL: https://github.com/zhengxiaoUVic/rmBayes
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: rmBayes results

Documentation:

Reference manual: rmBayes.pdf

Downloads:

Package source: rmBayes_0.1.16.tar.gz
Windows binaries: r-devel: rmBayes_0.1.16.zip, r-release: rmBayes_0.1.16.zip, r-oldrel: rmBayes_0.1.16.zip
macOS binaries: r-release (arm64): rmBayes_0.1.16.tgz, r-oldrel (arm64): rmBayes_0.1.16.tgz, r-release (x86_64): rmBayes_0.1.16.tgz, r-oldrel (x86_64): rmBayes_0.1.16.tgz
Old sources: rmBayes archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rmBayes to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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