Implement the Mash distance metric across the CLI, index, and compact vector traits. This includes adding a `Mash` variant to the `DistanceMetric` enum and `MetricArg` CLI argument, implementing the conversion from Jaccard distances using the standard mutation-rate estimator formula, and updating documentation with supported metrics and algorithmic references. Additionally, add an `entropy` method to rolling statistics for computing order-specific entropy.
Expands MatrixGroupOps with partial_group_min/max helpers for bitwise reductions and introduces add_col_from methods to persist external vectors as matrix columns. Refactors column aggregation in the partitioner to leverage these group operations directly, replacing iterative row processing with simplified builder lifecycle management and explicit metadata serialization.
Replace trait-based API documentation with concrete, zero-copy view structs and update all associated diagrams. Refine algorithmic descriptions for sentinel handling, overflow stores, and bulk operations. Clarify temporary file lifecycles and group-chunking strategies to support memory-efficient parallel aggregation.
Introduces `TempCompactIntVec` and `TempBitVec` as temporary, file-backed intermediates to replace eager in-memory vectors, enabling OS-level paging under memory pressure. Updates the `MatrixGroupOps` trait to return `io::Result` types, allowing proper error propagation and supporting chunked accumulation for large column groups. Includes builder patterns with `.freeze()` finalization, automatic `TempDir` cleanup on drop, and necessary test updates to handle the new fallible signatures. Also fixes `Cargo.toml` section ordering.
Add Mermaid diagrams to visualize the trait hierarchy, compact int storage layout, and SIMD-vectorizable arithmetic operations for MemoryIntVec and PersistentCompactIntVec. Also document concrete type structures and planned layer/partition composition rules to improve documentation clarity.
Document the two-tier compact integer encoding, BitSlice/IntSlice trait hierarchy, and SIMD-friendly O(n+k) algorithms. Include details on concrete memory and persistent vector types, matrix aggregation traits, and planned group-filtering APIs.
Eric Coissac