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>.

Some useful functions that can use 'grid' and 'ggplot2' to plot sectors and interact with 'Seurat' to plot gene expression percentages. Also, there are some examples of how to draw sectors in 'ComplexHeatmap'.

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>.

It provides a better alternative for stacked bar plot by creating a segmented total bar plot with custom annotations and labels. It is useful for visualizing the total of a variable and its segments in a single bar, making it easier to compare the segments and their contributions to the total.

It is an R package and web-based application, allowing users to perform interactive and reproducible visualizations of path diagrams for structural equation modeling (SEM) and networks using the 'ggplot2' engine. Its app (built with 'shiny') provides an interface that allows extensive customization, and creates CSV outputs, which can then be used to recreate the figures either using the web app or script-based workflow.