superb: Summary Plots with Adjusted Error Bars

Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superbPlot(), can either return a plot or a dataframe with the statistic and its precision interval so that other plotting package can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references.

Depends: R (≥ 3.5.0)
Imports: foreign, psych, plyr (≥ 1.8.4), ggplot2 (≥ 3.1.0), MASS, lsr (≥ 0.5), methods, Rdpack (≥ 0.7), stats, shiny, shinyBS, stringr, utils
Suggests: emojifont, fMultivar, grid, gridExtra, knitr, lattice, lawstat, boot, car, png, reshape2, rmarkdown, sadists, scales, testthat, tibble
Published: 2021-12-05
Author: Denis Cousineau [aut, cre], Bradley Harding [ctb], Marc-Andre Goulet [ctb], Jesika Walker [art, pre]
Maintainer: Denis Cousineau <denis.cousineau at>
License: GPL-3
NeedsCompilation: no
Citation: superb citation info
Materials: README NEWS
CRAN checks: superb results


Reference manual: superb.pdf
Vignettes: The making-of the figures in the article
Three steps to make your plot
Why use difference-adjusted confidence intervals?
Why use correlation-adjusted confidence intervals?
Using a custom statistic with its error bar
Devising custom plot layouts
Generating ready-to-analyze datasets with GRD
Unequal variances, Welch test, Tryon adjustments, and superb
(advanced) Alternate ways to decorrelate repeated measures from transformations
Illustrating Cohen's d with superb
Illustrating reference intervals with superb
Non-factorial within-subject designs in “superb“
Plotting proportions with “superb“
“superb“ and SPSS


Package source: superb_0.9.7.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): superb_0.9.7.8.tgz, r-release (x86_64): superb_0.9.7.8.tgz, r-oldrel: superb_0.9.7.8.tgz
Old sources: superb archive


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