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.

A one-stop shop for intuitive and dependency-free color and palette 
    creation and modification. Includes palettes and functionality from popular 
    packages such as 'viridis', 'RColorBrewer', and base R 'grDevices', as well 
    as 'ggplot2' plot bindings. Users can generate perceptually uniform and 
    colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, 
    map color gradients to value ranges, and create color-generating functions.