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

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>.

Produces odds ratio analyses with comprehensive reporting tools. Generates 
    plots, summary tables, and diagnostic checks for logistic regression models 
    fitted with 'glm()' using binomial family. Provides visualisation methods, 
    formatted reporting tables via 'gt', and tools to assess logistic regression 
    model assumptions.

Plot principal component histograms around a bivariate
        scatter plot.