Color and visualize wildlife distributions in
    space-time using raster data. In addition to enabling display of
    sequential change in distributions through the use of small multiples,
    'colorist' provides functions for extracting several features of
    interest from a sequence of distributions and for visualizing those
    features using HCL (hue-chroma-luminance) color palettes. Resulting
    maps allow for "fair" visual comparison of intensity values (e.g.,
    occurrence, abundance, or density) across space and time and can be
    used to address questions about where, when, and how consistently a
    species, group, or individual is likely to be found.

Call the 'DeOldify' <https://github.com/jantic/DeOldify> image
    colorization API on 'DeepAI'<https://deepai.org/machine-learning-model/colorizer>
    to colorize black and white images.

Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.

A tool for transforming coordinates in a color space to common
    color names using data from the Royal Horticultural Society and the
    International Union for the Protection of New Varieties of Plants.

Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).