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.

Color values in R are often represented as strings of hexadecimal
    colors or named colors.  This package offers fast conversion of 
    these color representations to either an array of red/green/blue/alpha values
    or to the packed integer format used in native raster objects.  Functions
    for conversion are also exported at the 'C' level for use in other packages.
    This fast conversion
    of colors is implemented using an order-preserving minimal perfect hash
    derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" 
    <doi:10.1093/comjnl/39.6.547>.

Extracts colors from various image types, returns customized reports and plots treemaps 
    and 3D scatterplots of image compositions. Color palettes can also be created.

Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters.

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.