Provides specialized visualization tools for Single-Case Experimental Design (SCED) 
    research using 'ggplot2'. SCED studies are a crucial methodology in behavioral and 
    educational research where individual participants serve as their own controls through 
    carefully designed experimental phases. This package extends 'ggplot2' to create 
    publication-ready graphics with professional phase change lines, support for multiple 
    baseline designs, and styling functions that follow SCED visualization conventions. 
    Key functions include adding phase change demarcation lines to existing plots and 
    formatting axes with broken axis appearance commonly used in single-case research.

A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.

Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The 'ggscidca' package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) <doi:10.1016/S1470-2045(14)71116-7>.

Contains 'ggplot2' geom for plotting brain atlases using 
    simple features. The largest component of the package is the data 
    for the two built-in atlases. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.

Mainly contains a plotting function ggseg3d(), 
    and data of two standard brain atlases (Desikan-Killiany and aseg). 
    By far, the largest bit of the package is the data for each of the atlases.
    The functions and data enable users to plot tri-surface mesh plots of
    brain atlases, and customise these by projecting colours onto the brain
    segments based on values in their own data sets. Functions are wrappers
    for 'plotly'. Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.