To be consistent with the rest of the
ggstatsverse, the Bayes Factor results are now always shown in favor of null over alternative (
pairwise_comparisons function gets
subject.id argument relevant for repeated measures design.
label column returned in
pairwise_comparisons now displays the p-value adjustment method in the label itself.
pairwise_caption function has changed its output to reflect changes made to the p-value labels.
Major internal refactoring to get rid of the following dependencies:
tidyr. This comes at the cost of omission of few of the details that were previously included in the output (e.g.,
mean.difference column for Student’s t-test).
Fixes a bug which affected results for within-subjects design when the dataframe wasn’t sorted by
This fix also now makes the results more consistent, such that irrespective of which type of statistics is chosen the
group2 columns are in identical order.
For repeated measures datasets with
NAs present, the Bayes Factor values were incorrect. This is fixed.
Internal refactoring to improve data wrangling using
Removes dependence on
jmv and instead relies on
PMCMRplus. This significantly reduces number of dependencies.
The non-parametric Dwass test has been changed to Dunn test.
Adapts to breaking changes in upcoming release of
Thanks to Sarah, the package has a hexsticker. :)
Due to changes made to downstream dependencies, the minimum R version expected is bumped to
Adds support for the Bayes Factor tests.
Exports the internal helper function
pairwise_comparisons_captionis removed since it was helpful only for
ggstatsplot’s internal graphics display and wasn’t of much utility outside of that context.
pairwise_comparisonsfunction now instead adds two columns (
p.value.adjustment) to all outputs specifying which test was carried out and which adjustment method is being used for p-value correction.
jmv 1.0.8, the results from the Dwass-Steel-Crichtlow-Fligner test will be slightly different.
p.value.label in the output dataframe has been renamed to
label to consider the possibility that Bayes Factor tests might also be supported in future.
The label now specified whether the p-value was adjusted or not for multiple comparisons.