Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for
    visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as 
    frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as 
    bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include
    but are not limited to: points with multiple uncertainty intervals, 
    eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>,
    density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>,
    quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>,
    complementary cumulative distribution function 
    barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>,
    and fit curves with multiple uncertainty ribbons.

Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.

Generate commonly used plots in the field of design of experiments using 'ggplot2'.
  'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot,
  regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs,
  contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.

A data visualization design that provides comparison between 
    two (Double) data sources (usually on a par with each other) on one 
    reformed heatmap, while inheriting 'ggplot2' features.

Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models. 
    See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

Provides a series of aliases to commonly used but difficult 
    to remember 'ggplot2' sequences.