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  "Title": "Reproducible Color Characterization of Digital Images for\nBiological Studies",
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  "Description": "Provides a standardized and reproducible framework for\ncharacterizing and classifying discrete color classes from\ndigital images of biological organisms. The package\nautomatically determines the presence or absence of 10\nhuman-visible color categories (black, blue, brown, green,\ngrey, orange, purple, red, white, yellow) using a\nbiologically-inspired Color Look-Up Table (CLUT) that\npartitions HSV color space. Supports both fully automated and\nsemi-automated (interactive) workflows with complete provenance\ntracking for reproducibility. Pre-processes images using the\n'recolorize' package (Weller et al. 2024\n<doi:10.1111/ele.14378>) for spatial-color binning, and\nintegrates with 'pavo' (Maia et al. 2019\n<doi:10.1111/2041-210X.13174>) for color pattern geometry\nstatistics. Designed for high-throughput analysis and seamless\nintegration with downstream evolutionary analyses.",
  "License": "MIT + file LICENSE",
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  "Date/Publication": "2026-04-10 05:56:55 UTC",
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