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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-subjects effects, rendering its relationship with the Bayes factor less clear in within-subjects (repeated-measures) designs. This urgent issue can be addressed by using within-subjects intervals in within-subjects designs, which integrate four methods including the Loftus-Masson (1994) <doi:10.3758/BF03210951>, the Rouder-Morey-Speckman-Province (2012) <doi:10.1016/j.jmp.2012.08.001>, 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.
Getting started
Browse package contents
Package details |
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| Author | Zhengxiao Wei [aut, cre] (<https://orcid.org/0000-0003-1866-2320>), Farouk S. Nathoo [aut] (<https://orcid.org/0000-0002-2569-3507>), Michael E.J. Masson [aut] (<https://orcid.org/0000-0002-5430-6078>) |
| Maintainer | Zhengxiao Wei <zhengxiao@uvic.ca> |
| License | GPL (>= 3) |
| Version | 0.1.15 |
| URL | https://github.com/zhengxiaoUVic/rmBayes |
| Package repository | View on CRAN |
| Installation |
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