It provides a custom 'ggplot2' geom to add day/night patterns to plots.
    It visually distinguishes daytime and nighttime periods. It is useful for 
    visualizing data that spans multiple days and for highlighting diurnal patterns.

Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment 
    made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>),
    the seasonal adjustment software officially recommended to the members of the European Statistical System and
    the European System of Central Banks.

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.

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.

Creates diagrams with an object-oriented approach. Geometric
    objects have computed properties with information about themselves
    (e.g., their area) or about their relationships with other objects
    (e.g, the distance between their edges). The objects have methods to
    convert them to geoms that can be plotted in 'ggplot2'.

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 one of the variables with a
    vector of distributions, and it will convert the visualisation into
    it's signal suppression counterpart.