Create Dendrograms and Tree Diagrams Using 'ggplot2'
This is a set of tools for dendrograms and
tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
clearly separate data from the presentation.
Unfortunately the plot method for dendrograms plots
directly to a plot device without exposing the data.
The 'ggdendro' package resolves this by making available
functions that extract the dendrogram plot data. The package
provides implementations for tree, rpart, as well as diana and agnes
cluster diagrams.
rpart(>= 4.0-0), tree, testthat, knitr, cluster

rpart(>= 4.0-0), tree, testthat, knitr, cluster, scales

This is a set of tools for dendrograms and
tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
clearly separate data from the presentation.
Unfortunately the plot method for dendrograms plots
directly to a plot device without exposing the data.
The 'ggdendro' package resolves this by making available
functions that extract the dendrogram plot data. The package
provides implementations for 'tree', 'rpart', as well as diana and agnes
(from 'cluster') diagrams.
rpart (>= 4.0-0), tree, testthat, knitr, cluster, scales,
spelling, rmarkdown

rpart (>= 4.0-0), tree, testthat, knitr, cluster, scales,
spelling, rmarkdown, covr

Interpretable Bivariate Density Visualization with ggplot2
c(person(given = "James",
family = "Otto",
role = c("aut", "cre"),
email = "jamesotto852@gmail.com",
comment = c(ORCID = "0000-0002-0665-2515")),
person(given = "David",
family = "Kahle",
role = c("aut"),
email = "david@kahle.io",
comment = c(ORCID = "0000-0002-9999-1558")))
The 'ggplot2' package provides simple functions for visualizing contours
of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators
as well as more interpretable visualizations based on highest density regions instead of
the traditional height of the estimated density surface.

Interpretable Bivariate Density Visualization with 'ggplot2'

isoband, vctrs, tibble, MASS, stats, scales

Visualizations of Distributions and Uncertainty
c(
person("Matthew", "Kay", role = c("aut", "cre"), email = "mjskay@umich.edu")
)
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.
plyr, 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
knitr, testthat, vdiffr (>= 0.3.0), svglite, broom (>=
0.4.3), modelr, cowplot, covr, gdtools, rmarkdown, ggrepel,
RColorBrewer, png, pkgdown
"ggdist-package.R" "binning_methods.R" "data.R"
"draw_key_slabinterval.R" "geom.R" "geom_slabinterval.R"
"geom_dotsinterval.R" "geom_interval.R" "geom_lineribbon.R"
"geom_pointinterval.R" "lkjcorr_marginal.R" "parse_dist.R"
"point_interval.R" "scales.R" "stat.R" "stat_slabinterval.R"
"stat_dist_slabinterval.R" "stat_sample_slabinterval.R"
"stat_dotsinterval.R" "stat_pointinterval.R" "stat_interval.R"
"stat_lineribbon.R" "student_t.R" "theme_ggdist.R"
"tidy_format_translators.R" "util.R"