A 'ggplot2' extension for visualising uncertainty with the goal
    of signal suppression. Usually, uncertainty visualisation focuses on
    expressing uncertainty as a distribution or probability, whereas
    'ggdibbler' differentiates itself by viewing an uncertainty
    visualisation as an adjustment to an existing graphic that
    incorporates the inherent uncertainty in the estimates.  You provide
    the code for an existing plot, but replace any of the variables with a
    vector of distributions, and it will convert the visualisation into
    it's signal suppression counterpart.

Provides 'ggplot2' extensions for creating dice-based visualizations 
    where each dot position represents a specific categorical variable. The package 
    includes geom_dice() for displaying presence/absence of categorical variables 
    using traditional dice patterns. Each dice position (1-6) represents a different 
    category, with dots shown only when that category is present. This allows 
    intuitive visualization of up to 6 categorical variables simultaneously.

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) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>,
    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.