Changelog of @hackage/pure-noise 0.2.1.0

Changelog for `pure-noise`

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to the Haskell Package Versioning Policy.

1D noise support with noise1At evaluation function
Noise slicing functions (sliceX2, sliceY2, sliceX3, sliceY3, sliceZ3) for reducing noise dimensionality
Comprehensive Haddock documentation for:
- Core Noise p v type with usage examples
- All utility functions (warp, reseed, remap, blend)
- All slicing functions with examples
Exported utility functions:
- warp - transform coordinate space
- reseed - modify seed for independent layers (generalizes alterSeed2/alterSeed3)
- remap - transform noise values (alias for fmap)
- blend - combine noise functions with custom blending (alias for liftA2)
Exported Noise type for advanced usage and type annotations

Refactored to unified noise representation using newtype Noise p v
- Noise2 and Noise3 are now type aliases: Noise (a,a) a and Noise (a,a,a) a
- Provides Functor, Applicative, Monad, Num, Fractional, and Floating instances
- All dimensions share the same instance implementations for consistency
Updated module documentation with examples of new features (slicing, warping, layering)
Improved README with advanced usage examples

Overall: 90% of benchmarks improved with +32.4% average performance gain
Ping-pong fractals: Branchless optimization yields 110-148% improvement (perlin, value, openSimplex)
Cellular noise: REWRITE RULES for gradient lookups provide 61-70% improvement
3D ValueCubic fractals: Fixed performance regression, achieving 45-80% improvement
Perlin noise: 10-63% improvements from optimized lerp/cubic interpolation with REWRITE RULES
Value noise: 11-43% improvements from interpolation optimizations
OpenSimplex2: 6-16% improvement for Float; minor regression (~5%) for Double variants
SuperSimplex2: Shows 3-21% regression due to improved benchmark methodology
- Previous benchmarks used same X/Y offset (diagonal sampling), which favored SuperSimplex's triangular lattice
- New benchmarks use independent X/Y offsets for realistic 2D coordinate distributions
- Algorithm remains functionally correct; numbers now reflect true 2D performance

Comprehensive haddock documentation for main Numeric.Noise module with usage examples

Migrated internal implementation from vector to primitive (PrimArray)
Removed vector dependency from library (still used in benchmarks)
Require GHC 9.2+ (base >= 4.16)
Hide internal modules from public API (Numeric.Noise.Internal, Numeric.Noise.Internal.Math)
Improved cellular noise performance by 20-30% through specialized computation paths for different result types