Provides multiple palettes based on pride flags with tailored themes.

Visualise overlapping time series lines as a heatmap of line
    density. Provides a 'ggplot2' statistic implementing the DenseLines
    algorithm, which "normalizes time series by the arc length to compute
    accurate densities"
    (Moritz and Fisher, 2018) <doi:10.48550/arXiv.1808.06019>.

Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics.  
  Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.

Provides a way to log 'ggplot' component calls, which can be
    useful for debugging and understanding how 'ggplot' objects are created.
    The logged calls can be printed, saved, and re-executed to reproduce the
    original 'ggplot' object.

Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.

Create network-style visualizations of pairwise relationships
    using custom edge glyphs built on top of 'ggplot2'. The package
    supports both statistical and non-statistical data and allows users to
    represent directed relationships. This enables clear,
    publication-ready graphics for exploring and communicating relational
    structures in a wide range of domains. The method was first used in
    Abu-Akel et al. (2021) <doi:10.1371/journal.pone.0245100>. Code is
    released under the MIT License; included datasets are licensed under
    the Creative Commons Attribution 4.0 International (CC BY 4.0).

Provides functions for defining 
    mixed graphs containing three types of edges, directed, 
    undirected and bi-directed, with possibly multiple edges.
    These graphs are useful because they capture fundamental
    independence structures in multivariate distributions
    and in the induced distributions after marginalization 
    and conditioning.
    The package is especially concerned with Gaussian graphical
    models for
    (i) ML estimation for directed acyclic graphs, undirected and 
    bi-directed graphs and ancestral graph models
    (ii) testing several conditional independencies
    (iii) checking global identification of DAG Gaussian models
    with one latent variable
    (iv) testing Markov equivalences and generating Markov 
    equivalent graphs of specific types.