rmBayes

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.

Last 30 days

This package has been downloaded 250 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,951 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Feb 11, 2025 with 53 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.