Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'.

Interactively edit 'ggplot2' layer and theme aesthetics definitions.

Compute marginal effects and adjusted predictions from statistical
    models and returns the result as tidy data frames. These data frames are 
    ready to use with the 'ggplot2'-package. Effects and predictions can be 
    calculated for many different models. Interaction terms, splines and 
    polynomial terms are also supported. The main functions are ggpredict(), 
    ggemmeans() and ggeffect(). There is a generic plot()-method to plot the 
    results using 'ggplot2'.

Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.

Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.

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/>.

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'.