Examines the characteristics of a data frame and a formula to
    automatically choose the most suitable type of plot out of the following supported
    options: scatter, violin, box, bar, density, hexagon bin, spine plot, and
    heat map. The aim of the package is to let the user focus on what to plot,
    rather than on the "how" during exploratory data analysis. It also automates
    handling of observation weights, logarithmic axis scaling, reordering of
    factor levels, and overlaying smoothing curves and median lines. Plots are
    drawn using 'ggplot2'.

Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.

Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.

Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.

Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.

Plot marginal effects for interactions estimated
    from linear models.

The main function, plot_mm(), is used for (gg)plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models in line with the
    tidy principles. The package includes several additional functions for added plot customization. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. 
    Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, 
    logistic regressions, linear regressions, and Poisson regressions.

Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.

Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis,
  <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.