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>.

Annotation helper functions for publication-quality 'ggplot2' 
    visualisation. These functions make it easier to annotate plots in 
    a way that stays consistent with the set theme.

Provides a 'ggplot2' geom and position for visualizing brain
    region data on cortical, subcortical, and white matter tract atlases.
    Brain atlas geometries are stored as polygon data, enabling
    seamless integration with the 'ggplot2' ecosystem including faceting,
    custom scales, and themes.  Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.

Provides the 'ggseg_atlas' S3 class used across the 'ggsegverse'
    ecosystem for 2D and 3D brain visualisation. Ships four bundled atlases
    ('Desikan-Killiany', 'FreeSurfer' 'aseg', 'TRACULA', 'SUIT') and functions
    for querying, subsetting, renaming, and enriching atlas objects. Also
    includes readers for 'FreeSurfer' statistics files.

Provides additional brain surface meshes for cortical and
    cerebellar visualisation in the 'ggsegverse' ecosystem. Cortical surfaces
    include pial, white, midthickness, semi-inflated, sphere, smoothwm, and
    orig at fsaverage5 resolution. Cerebellar surfaces include the Spatially
    Unbiased Infratentorial Template (SUIT) flatmap. All meshes follow the
    same vertices/faces data frame format used by 'ggseg.formats' and
    'ggseg3d'.