Package index • ggdist

Package overview
	`ggdist-package` `ggdist`	Visualizations of Distributions and Uncertainty
Stats and Geoms for visualizing distributions with ggplot2 These ggplot2 layers are split into two categories: `stat`s for visualizing sample data and distribution vectors, and the underlying `geom`s for visualizing already-summarized data (e.g., intervals or density values).
Stats for visualizing sample data and distribution vectors `stat`s for visualizing tidy data frames of draws (e.g. sample data, draws from a bootstrap distributation, draws from a Bayesian posterior) and distribution vectors (such as distributional objects and `posterior::rvar()`s).
	`stat_slabinterval()`	Slab + interval plots for sample data and analytical distributions (ggplot stat)
	`stat_pointinterval()`	Point + multiple-interval plot (shortcut stat)
	`stat_interval()`	Multiple-interval plot (shortcut stat)
	`stat_slab()`	Slab (ridge) plot (shortcut stat)
	`stat_halfeye()`	Half-eye (density + interval) plot (shortcut stat)
	`stat_eye()`	Eye (violin + interval) plot (shortcut stat)
	`stat_ccdfinterval()`	CCDF bar plot (shortcut stat)
	`stat_cdfinterval()`	CDF bar plot (shortcut stat)
	`stat_gradientinterval()`	Gradient + interval plot (shortcut stat)
	`stat_histinterval()`	Histogram + interval plot (shortcut stat)
	`stat_spike()`	Spike plot (ggplot2 stat)
	`stat_dotsinterval()`	Dots + point + interval plot (shortcut stat)
	`stat_dots()`	Dot plot (shortcut stat)
	`stat_mcse_dots()`	Blurry MCSE dot plot (stat)
	`stat_lineribbon()`	Line + multiple-ribbon plot (shortcut stat)
	`stat_ribbon()`	Multiple-ribbon plot (shortcut stat)
Geoms for visualizing already-summarized data `geom`s for visualizing data that has already been summarized (e.g. into density function values, points, intervals, etc).
	`geom_slabinterval()`	Slab + point + interval meta-geom
	`geom_pointinterval()`	Point + multiple-interval plot (shortcut geom)
	`geom_interval()`	Multiple-interval plot (shortcut geom)
	`geom_slab()`	Slab (ridge) plot (shortcut geom)
	`geom_spike()`	Spike plot (ggplot2 geom)
	`geom_dotsinterval()`	Automatic dotplot + point + interval meta-geom
	`geom_dots()`	Dot plot (shortcut geom)
	`geom_blur_dots()`	Blurry dot plot (geom)
	`geom_weave()`	Dot-weave plot (shortcut geom)
	`geom_swarm()`	Beeswarm plot (shortcut geom)
	`geom_lineribbon()`	Line + multiple-ribbon plots (ggplot geom)
Point summaries and intervals Functions to calculate point summaries and intervals on tidy data. These functions can be used directly on vectors, on data frames of draws, on distribution vectors (such as distributional objects and `posterior::rvar()`s), and in ggdist `stat`s and `geom`s to determine what point summaries and intervals are drawn.
	`point_interval()` `qi()` `ll()` `ul()` `hdi()` `Mode()` `hdci()` `mean_qi()` `median_qi()` `mode_qi()` `mean_ll()` `median_ll()` `mode_ll()` `mean_ul()` `median_ul()` `mode_ul()` `mean_hdi()` `median_hdi()` `mode_hdi()` `mean_hdci()` `median_hdci()` `mode_hdci()`	Point and interval summaries for tidy data frames of draws from distributions
	`curve_interval()`	Curvewise point and interval summaries for tidy data frames of draws from distributions
	`interval_widths()` `pretty_widths()`	Nicely-spaced sets of interval widths
	`cut_cdf_qi()`	Categorize values from a CDF into quantile intervals
Distributions Helper functions for manipulating and visualizing analytical distributions.
	`parse_dist()` `r_dist_name()`	Parse distribution specifications into columns of a data frame
	`dstudent_t()` `pstudent_t()` `qstudent_t()` `rstudent_t()`	Scaled and shifted Student's t distribution
	`dlkjcorr_marginal()` `plkjcorr_marginal()` `qlkjcorr_marginal()` `rlkjcorr_marginal()`	Marginal distribution of a single correlation from an LKJ distribution
	`marginalize_lkjcorr()`	Turn spec for LKJ distribution into spec for marginal LKJ distribution
ggplot2 components ggplot2 scales, positions, sub-guides, and themes designed for use with ggdist `stat`s and `geom`s.
Scales and sub-scales Scales for custom aesthetics used by ggdist geoms and stats, such as `thickness`, `colour_ramp`, and `side`, as well as sub-geometry-specific aesthetics, such as `slab_colour`, `point_size`, etc.
	`scale_thickness_shared()` `scale_thickness_identity()`	Slab thickness scale (ggplot2 scale)
	`thickness()`	Thickness (datatype)
	`subscale_thickness()`	Sub-scale for thickness aesthetic
	`subscale_identity()`	Identity sub-scale for thickness aesthetic
	`scale_colour_ramp_continuous()` `scale_color_ramp_continuous()` `scale_colour_ramp_discrete()` `scale_color_ramp_discrete()` `scale_fill_ramp_continuous()` `scale_fill_ramp_discrete()`	Secondary color scale that ramps from another color (ggplot2 scale)
	`scale_side_mirrored()`	Side scale for mirrored slabs (ggplot2 scale)
	`scale_point_colour_discrete()` `scale_point_color_discrete()` `scale_point_colour_continuous()` `scale_point_color_continuous()` `scale_point_fill_discrete()` `scale_point_fill_continuous()` `scale_point_alpha_continuous()` `scale_point_alpha_discrete()` `scale_point_size_continuous()` `scale_point_size_discrete()` `scale_interval_colour_discrete()` `scale_interval_color_discrete()` `scale_interval_colour_continuous()` `scale_interval_color_continuous()` `scale_interval_alpha_continuous()` `scale_interval_alpha_discrete()` `scale_interval_size_continuous()` `scale_interval_size_discrete()` `scale_interval_linetype_discrete()` `scale_interval_linetype_continuous()` `scale_slab_colour_discrete()` `scale_slab_color_discrete()` `scale_slab_colour_continuous()` `scale_slab_color_continuous()` `scale_slab_fill_discrete()` `scale_slab_fill_continuous()` `scale_slab_alpha_continuous()` `scale_slab_alpha_discrete()` `scale_slab_size_continuous()` `scale_slab_size_discrete()` `scale_slab_linewidth_continuous()` `scale_slab_linewidth_discrete()` `scale_slab_linetype_discrete()` `scale_slab_linetype_continuous()` `scale_slab_shape_discrete()` `scale_slab_shape_continuous()` `guide_colourbar2()` `guide_colorbar2()`	Sub-geometry scales for geom_slabinterval (ggplot2 scales)
	`partial_colour_ramp()`	Partial colour ramp (datatype)
	`ramp_colours()`	Apply partial colour ramps
Positions Positions for dodging `geom`s and `stat`s.
	`position_dodgejust()`	Dodge overlapping objects side-to-side, preserving justification
Guides and sub-guides Guide for labeling the `colour_ramp` and `fill_ramp` scales, and sub-guides for labelling the `thickness` axis in `geom_slab()` and dot counts in `geom_dots()`.
	`guide_rampbar()`	Continuous guide for colour ramp scales (ggplot2 guide)
	`subguide_axis()` `subguide_inside()` `subguide_outside()` `subguide_integer()` `subguide_count()` `subguide_slab()` `subguide_dots()` `subguide_spike()`	Axis sub-guide for thickness scales
	`subguide_none()`	Empty sub-guide for thickness scales
Themes ggdist theme and helper functions for ggplot2.
	`theme_ggdist()` `theme_tidybayes()` `facet_title_horizontal()` `axis_titles_bottom_left()` `facet_title_left_horizontal()` `facet_title_right_horizontal()`	Simple, light ggplot2 theme for ggdist and tidybayes
Estimators and algorithms Modular and configurable estimators and algorithms used by a variety of `stat`s and `geom`s. Many of these functions use automatic partial function application (aka “currying”) provided by `auto_partial()`.
	`auto_partial()`	Automatic partial function application in ggdist
	`waiver()`	A waived argument
Dotplot layout algorithms Algorithms used by `geom_dotsinterval()` for automatically determining nice-looking dotplot binwidths and laying out dots.
	`find_dotplot_binwidth()`	Dynamically select a good bin width for a dotplot
	`bin_dots()`	Bin data values using a dotplot algorithm
Dotplot smoothers Smoothers for creating “density dotplots” using `geom_dotsinterval()`.
	`smooth_bounded()` `smooth_unbounded()`	Smooth dot positions in a dotplot using a kernel density estimator ("density dotplots")
	`smooth_discrete()` `smooth_bar()`	Smooth dot positions in a dotplot of discrete values ("bar dotplots")
	`smooth_none()`	Apply no smooth to a dotplot
Density estimators Density functions used by `stat_slabinterval()` and by the density smoothers for dotplot layout.
	`density_bounded()`	Bounded density estimator using the reflection method
	`density_unbounded()`	Unbounded density estimator
	`density_histogram()`	Histogram density estimator
Bandwidth estimators Bandwidth estimators for use with `density_unbounded()` and `density_bounded()`.
	`bandwidth_nrd0()` `bandwidth_nrd()` `bandwidth_ucv()` `bandwidth_bcv()` `bandwidth_SJ()` `bandwidth_dpi()`	Bandwidth estimators
Distribution bounds estimators Distribution bounds estimators for use with `density_bounded()`.
	`bounder_cdf()`	Estimate bounds of a distribution using the CDF of its order statistics
	`bounder_cooke()`	Estimate bounds of a distribution using Cooke's method
	`bounder_range()`	Estimate bounds of a distribution using the range of the sample
Histogram layout algorithms Algorithms for bin selection and alignment for use with `density_histogram()`.
	`breaks_fixed()` `breaks_Sturges()` `breaks_Scott()` `breaks_FD()` `breaks_quantiles()`	Break (bin) selection algorithms for histograms
	`align_none()` `align_boundary()` `align_center()`	Break (bin) alignment methods
Blur functions Blur functions for use with `geom_blur_dots()`.
	`blur_gaussian()` `blur_interval()`	Blur functions for blurry dot plots
Weighted quantiles and CDFs Weighted generalizations of `quantile()` and `ecdf()` used internally by several algorithms.
	`weighted_quantile()` `weighted_quantile_fun()`	Weighted sample quantiles
	`weighted_ecdf()`	Weighted empirical cumulative distribution function
Probability expressions Experimental mini-domain-specific language for probability expressions in `stat_slabinterval()` aesthetics.
	`Pr_()` `p_()`	Probability expressions in ggdist aesthetics
Compatibility with other packages Functions to convert the ggdist naming scheme (for `point_interval()`) to and from other packages’ naming schemes.
	`to_broom_names()` `from_broom_names()` `to_ggmcmc_names()` `from_ggmcmc_names()`	Translate between different tidy data frame formats for draws from distributions
Datasets Datasets used for examples and testing
	`RankCorr` `RankCorr_u_tau`	Thinned subset of posterior sample from a Bayesian analysis of perception of correlation.
Deprecated functions
	`ggdist-deprecated` `StatSampleSlabinterval` `stat_sample_slabinterval` `StatDistSlabinterval` `stat_dist_slabinterval` `stat_dist_halfeye` `stat_dist_eye` `stat_dist_ccdfinterval` `stat_dist_cdfinterval` `stat_dist_gradientinterval` `stat_dist_slab` `stat_dist_pointinterval` `stat_dist_interval` `stat_dist_lineribbon` `stat_dist_dotsinterval` `stat_dist_dots`	Deprecated functions and arguments in ggdist

Reference

Package overview

Stats and Geoms for visualizing distributions with ggplot2

Stats for visualizing sample data and distribution vectors

Geoms for visualizing already-summarized data

Point summaries and intervals

Distributions

ggplot2 components

Scales and sub-scales

Positions

Guides and sub-guides

Themes

Estimators and algorithms

Dotplot layout algorithms

Dotplot smoothers

Density estimators

Bandwidth estimators

Distribution bounds estimators

Histogram layout algorithms

Blur functions

Weighted quantiles and CDFs

Probability expressions

Compatibility with other packages

Datasets

Deprecated functions