Provides 'ggplot2' extensions for creating dice-based visualizations 
    where each dot position represents a specific categorical variable. The package 
    includes geom_dice() for displaying presence/absence of categorical variables 
    using traditional dice patterns. Each dice position (1-6) represents a different 
    category, with dots shown only when that category is present. This allows 
    intuitive visualization of up to 6 categorical variables simultaneously.

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.

A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the 'ggdmc' package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>, and Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.