Compare color palettes with simulations of color vision deficiencies - deuteranopia, protanopia, and tritanopia.
        It includes calculation of distances between colors, and creating summaries of differences between a color palette and simulations of color vision deficiencies.
        This work was inspired by the blog post at <http://www.vis4.net/blog/2018/02/automate-colorblind-checking/>.

Provide the safe color set for color blindness, 
         the simulator of protanopia, deuteranopia.
         The color sets are collected from:
         Wong, B. (2011) <doi:10.1038/nmeth.1618>, and
         <http://mkweb.bcgsc.ca/biovis2012/>.
         The simulations of the appearance of the colors to color-deficient
         viewers were based on algorithms in
         Vienot, F., Brettel, H. and Mollon, J.D. (1999) 
         <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>.
         The cvdPlot() function to generate 'ggplot' grobs of simulations 
         were modified from <https://github.com/clauswilke/colorblindr>.

Colorful Data Frames in the terminal. The new class does
             change the behaviour of any of the objects, but adds a style
             definition and a print method. Using ANSI escape codes, it
             colors the terminal output of data frames. Some column types
             (such as p-values and identifiers) are automatically
             recognized.

Loads and displays images, selectively masks specified background
    colors, bins pixels by color using either data-dependent or
    automatically generated color bins, quantitatively measures color
    similarity among images using one of several distance metrics for
    comparing pixel color clusters, and clusters images by object color
    similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written
    for use with organism coloration (reef fish color diversity, butterfly
    mimicry, etc), but easily applicable for any image set.

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
    This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation).  The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.