The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.

An extension to 'ggplot2' and 'magick'. It contains 
    three groups of functions: Functions in the first group draw 'ggplot2' - based plots: geom_shading_bar() draws barplot 
	with shading colors in each bar. geom_rect_cm(), geom_circle_cm() and geom_ellipse_cm() draw rectangles, circles 
	and ellipses with centimeter as their unit. Thus their sizes do not change when the coordinate system or the aspect ratio 
	changes. annotation_transparent_text() draws labels with transparent texts. annotation_shading_polygon() draws irregular 
	polygons with shading colors. Functions in the second group generate coordinates for regular shapes and make linear transformations. Functions in the third group are 'magick' - based functions facilitating image processing.

Hidden Markov Models are useful for modeling
 sequential data. This package provides several functions
 implemented in C++ for explaining the algorithms used for
 Hidden Markov Models (forward, backward, decoding,
 learning).

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'.