ggpmisc >= 0.5.0 updates

ggpmisc: Miscellaneous Extensions to ‘ggplot2’

R Package Update
Author

Pedro J. Aphalo

Published

2022-08-05

Modified

2024-04-07

Keywords

ggpmisc pkg

Note

Since moving the site to Quarto I am updating this post to give a concise overview of changes. Differences between versions are listed in detail in the NEWS file.

Overview of changes

Version 0.5.6 fixes bugs and implements enhancements to stat_multcomp() and tracks changes in ‘ggplot2’ >= 3.5.0. Depends directly on ‘ggpp’ >= 0.5.7, and indirectly on ‘ggplot2’ >= 3.5.0.

Version 0.5.5 fixes bug that prevented use of model formulas with a transformation in the lhs, such as use of I() or other functions instead of bare y or x.

Version 0.5.4 brings stat_multcomp() for computing multiple comparisons within the framework of general linear hypothesis as implemented in R package ‘multcomp’ and annotation of plots with the outcomes.

Version 0.5.3 fixes bugs, brings compatibility with package ‘gganimate’ and adds parameter n.min to all statistics that are based on model fitting or correlation testing functions. Arguments passed to n.min make it possible to increase the previously hard-coded limit that remains in most cases as the default.

Version 0.5.2 fixes bugs and ensures compatibility with updates to ‘ggplot2’ and ‘lubridate’.

Version 0.5.1 brings additional enhancements to the annotations based on model fits to improve traceability. New scales scale_colour_logFC(), scale_color_logFC() and scale_fill_logFC() and revised scale_colour_outcome() and scale_fill_outcome() add flexibility.

Version 0.5.0 brings enhancements to the annotations based on model fits. The most visible change is the new convenience function use_label() that greatly simplifies the assembly of labels from components and their mapping to aesthetics. Function stat_correlation() now computes confidence intervals for correlation estimates. New functions keep_tidy(), keep_glance() and keep_augment() are wrappers on methods tidy(), glance() and augment() from package ‘broom’. These new functions make it possible to keep a trace of the origin of the “broom-tidied” outputs similarly as it is possible with "lm" objects and other objects returned by R’s model fitting functions.

Documentation web site includes all help pages, with output from all examples and vignettes in HTML format .

Please raise issues concerning bugs or enhancements to this package through GitHub at https://github.com/aphalo/ggpmisc/issues. Pull requests are also welcome.

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