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>.

Version: 0.9.4.2
Depends: R (≥ 3.5.0)
Imports: plyr (≥ 1.8.4), ggplot2 (≥ 3.1.0), lsr (≥ 0.5), Rdpack (≥ 0.7), stats, ggsci, MASS, matrixcalc
Suggests: grid, gridExtra, knitr, rmarkdown, car, fMultivar, lattice, lawstat, reshape2, testthat, schoRsch, emojifont, sadists
Published: 2021-03-20
Author: Denis Cousineau [aut, cre], Bradley Harding [ctb], Marc-André Goulet [ctb]
Maintainer: Denis Cousineau <denis.cousineau at uottawa.ca>
License: GPL-3
NeedsCompilation: no
Citation: superb citation info
Materials: README NEWS
CRAN checks: superb results

Downloads:

Reference manual: superb.pdf
Vignettes: 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
Package source: superb_0.9.4.2.tar.gz
Windows binaries: r-devel: superb_0.9.4.2.zip, r-release: superb_0.9.4.2.zip, r-oldrel: superb_0.9.4.2.zip
macOS binaries: r-release: superb_0.9.4.2.tgz, r-oldrel: superb_0.9.4.2.tgz

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