Audits 'ggplot2' visualizations for accessibility issues, misleading 
    practices, and readability problems. Checks for color accessibility concerns 
    including colorblind-unfriendly palettes, misleading scale manipulations such 
    as truncated axes and dual y-axes, text readability issues like small fonts 
    and overlapping labels, and general accessibility barriers. Provides 
    comprehensive audit reports with actionable suggestions for improvement.
    Color vision deficiency simulation uses methods from the 'colorspace' 
    package Zeileis et al. (2020) <doi:10.18637/jss.v096.i01>. Contrast 
    calculations follow WCAG 2.1 guidelines (W3C 2018 
    <https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum>).

Extends the 'ggplot2' error geoms.
    geom_error() accepts an error aesthetic with auto-inference of the orientation.
    It also supports `error_neg` and `error_pos` for asymmetric cases, with full control over aesthetics per side, such as color, width etc...

Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
    This package is part of the 'rethomics' framework <https://rethomics.github.io/>.

Provides 'ggplot2'-based visualization functions for output objects
    from the 'exametrika' package, which implements test data engineering methods
    described in Shojima (2022, ISBN:978-981-16-9547-1). Supports a wide range of
    psychometric models including Item Response Theory, Latent Class Analysis,
    Latent Rank Analysis, Biclustering (binary, ordinal, and nominal),
    Bayesian Network Models, and related network models. All plot functions
    return 'ggplot2' objects that can be further customized by the user.

Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.

Readable, complete and pretty graphs for correspondence analysis made 
    with 'FactoMineR'. They can be rendered as interactive 'HTML' plots, showing useful
    informations at mouse hover. The interest is not mainly visual but statistical:
    it helps the reader to keep in mind the data contained in the cross-table or Burt
    table while reading the correspondence analysis, thus preventing over-interpretation.
    Most graphs are made with 'ggplot2', which means that you can use the + syntax to 
    manually add as many graphical pieces you want, or change theme elements. 3D 
    graphs are made with 'plotly'.