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Changelog

Source: NEWS.md

huerd 0.4.0 (2025-07-06)

Major Changes

New Features

  • Added reproducibility system for palette generation with metadata storage.

  • Added hue order reversal detection for CVD analysis.

Breaking Changes

  • Removed new_device, device_width, and device_height parameters from plot_palette_analysis().

  • Moved gridExtra from Suggests to Imports, added grid to Imports.

  • Removed helper functions: .create_graphics_device, .is_ragg_available, .is_interactive_ide, calculate_safe_margins.

huerd 0.3.1 (2025-07-04)

New Features

  • Added automated font size scaling to plot_palette_analysis() that automatically adjusts text sizes based on device dimensions.

huerd 0.3.0 (2025-07-04)

Major Changes

  • Multi-optimizer framework: The generate_palette() function now supports four different optimization algorithms through the new optimizer parameter:
    • "nloptr_cobyla" (default): Deterministic optimization with constraint handling
    • "sann": Stochastic simulated annealing
    • "nlopt_direct": Deterministic global optimization using DIRECT algorithm
    • "nlopt_neldermead": Derivative-free local optimization using Nelder-Mead simplex
  • Added automatic font scaling to plot_palette_analysis() that prevents text overlap and out-of-frame issues.

Minor Changes

  • Added internal utility functions .hex_to_oklab() and .oklab_to_hex() to eliminate repetitive color conversion patterns.

  • Fixed failing visualization tests and added test coverage for new utility functions.

  • Updated test files to use utility functions.

huerd 0.1.1 (2025-07-01)

Minor Changes

  • Removed unused cli and crayon dependencies.

huerd 0.1.0 (2025-07-01)

This is a major refactoring and simplification of huerd, focusing the package on a single, scientifically-grounded objective: pure minimax color palette generation.

Major Changes

  • Pure minimax optimization: The core generate_palette() function has been streamlined to use a pure minimax objective, maximizing the minimum perceptual distance between colors in the OKLAB space. All complex multi-objective parameters (optimize_for, balance_weights, aesthetic_penalty_weights_LC) have been removed, simplifying the API and aligning the package with a clear, defensible scientific goal.

  • Comprehensive diagnostic dashboard: A new plot_palette_analysis() function has been added. It provides a comprehensive, six-panel diagnostic dashboard inspired by scicomap for in-depth palette analysis. This feature uses only base R graphics and introduces zero new dependencies.

  • Automatic brightness sorting: All palettes generated by generate_palette() are now automatically sorted by their OKLAB lightness value, ensuring intuitive and consistent ordering from darkest to lightest.

  • Simplified evaluation: The evaluate_palette() function is now a pure data provider, returning raw, objective metrics (distances, CVD safety, color distribution) without any subjective heuristic scoring.

Minor Changes

  • The is_cvd_safe() function now uses the more robust evaluate_palette() function for its calculations.
  • The print methods for huerd_palette and huerd_evaluation have been updated to reflect the new, simplified data structures.

Removals

The following experimental and non-essential features have been removed to streamline the package:

  • Force-field and repulsion-based optimization algorithms.
  • Palette animation framework.
  • Algorithm comparison tools.
  • Post-hoc CVD refinement steps.
  • 3D color space visualizations.
  • All functionality related to the CAM16 and CIECAM16 color models.

huerd 0.0.3 (2025-06-22)

  • Added a post-hoc cvd_adjustment step to improve palettes for color vision deficiency.
  • Added a README.md with basic usage instructions.

huerd 0.0.2 (2025-01-12)

  • Switched the core optimization algorithm to an inverse-square law repulsion model.
  • Removed all functionality related to the CIECAM16 color model.
  • Added a swatchplot to the animation feature.

huerd 0.0.1 (2025-01-04)

  • Initial release.
  • Core functionality for palette generation using the CAM16 and CIECAM16 color models.
  • Features for color space conversion and animation of the optimization process.