superb: Get Precision of Means Under Various Designs and Sampling Schemes

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 (2017) <doi:10.5709/acp-0214-z> for a review or 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>.

Depends: R (≥ 3.5.0)
Imports: plyr (≥ 1.8.4), ggplot2 (≥ 3.1.0), MASS, lsr (≥ 0.5), Rdpack (≥ 0.7), stats
Suggests: car, emojifont, fMultivar, grid, gridExtra, knitr, lattice, lawstat, reshape2, rmarkdown, sadists, testthat
Published: 2021-04-21
Author: Denis Cousineau [aut, cre], Bradley Harding [ctb], Marc-Andre Goulet [ctb], Jesika Walker [art]
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
Package source: superb_0.9.5.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: superb_0.9.5.0.tgz, r-oldrel: superb_0.9.5.0.tgz
Old sources: superb archive


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