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.

scatterplot3d, plotly, gplots, abind, magrittr, scales

scatterplot3d, plotly, gplots, abind, magrittr, scales, qpdf

Imports: jpeg, png, stats, clue, ape, mgcv, emdist, scatterplot3d,
plotly, gplots, abind, magrittr, scales, qpdf, spatstat.geom

Title: Implementation of Colored Independent Component Analysis and
Spatial Colored Independent Component Analysis
Author: Lee, S., Shen, H., Truong, Y. and Zanini, P.
Description: It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.

Title: Simulate Temporally Autocorrelated Population Time Series
Authors@R: c(person("Julia", "Pilowsky", email = "jap2178@caa.columbia.edu",
person("Elizabeth", "Crone", email = "elizabeth.crone@tufts.edu",
Description: 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>. The simulations are based on an assumption of an asexually reproducing population, but it can also be used to simulate females of a sexually reproducing species.