ggdist: Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <doi:10.1111/1467-985X.00120>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.

Version: 2.2.0
Depends: R (≥ 3.5.0)
Imports: dplyr (≥ 0.8.0), tidyr (≥ 1.0.0), ggplot2 (≥ 3.3.0), purrr (≥ 0.2.3), rlang (≥ 0.3.0), scales, grid, forcats, HDInterval, tibble, vctrs, distributional
Suggests: knitr, testthat, vdiffr (≥ 0.3.0), svglite, broom (≥ 0.5.6), modelr, cowplot, covr, gdtools, rmarkdown, png, pkgdown
Published: 2020-07-12
Author: Matthew Kay [aut, cre]
Maintainer: Matthew Kay <mjskay at umich.edu>
BugReports: https://github.com/mjskay/ggdist/issues/new
License: GPL (≥ 3)
URL: http://mjskay.github.io/ggdist, https://github.com/mjskay/ggdist
NeedsCompilation: no
Language: en-US
Citation: ggdist citation info
Materials: NEWS
CRAN checks: ggdist results

Downloads:

Reference manual: ggdist.pdf
Vignettes: Frequentist uncertainty visualization
Slab + interval stats and geoms
Package source: ggdist_2.2.0.tar.gz
Windows binaries: r-devel: ggdist_2.2.0.zip, r-release: ggdist_2.2.0.zip, r-oldrel: ggdist_2.1.1.zip
macOS binaries: r-release: ggdist_2.2.0.tgz, r-oldrel: ggdist_2.1.1.tgz
Old sources: ggdist archive

Reverse dependencies:

Reverse imports: multinma, tidybayes
Reverse suggests: distributional

Linking:

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