⬆️ version bump to v4.5

- Update obioptions.Version from "Release 4.4.29" to "/v/ Release v5"
- Update version.txt from 4.29 → .30
(automated by Makefile)

autodoc/docmd/pkg/obidist/dist_matrix.md

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+# `obidist` Package: Efficient Symmetric Distance/Similarity Matrix Management
+
+The `*DistMatrix` type provides a memory-efficient, symmetric matrix implementation for distance or similarity data.
+
+- **Storage Strategy**: Only the upper triangle (i < j) is stored, reducing memory usage from *O(n²)* to *n(n−1)/2*.
+- **Diagonal Handling**: Diagonal entries are fixed (0.0 for distances, 1.0 for similarities); assignments to diagonal indices are silently ignored.
+- **Symmetry Guarantee**: `Get(i, j)` and `Set(i, j, v)` automatically handle both (i,j) and (j,i), ensuring consistency.
+
+## Constructors
+
+| Function | Description |
+|---------|-------------|
+| `NewDistMatrix(n)` / `WithLabels(labels)` | Creates *n×n* distance matrix (diag = 0). |
+| `NewSimilarityMatrix(n)` / `WithLabels(labels)` | Creates *n×n* similarity matrix (diag = 1). |
+
+## Core Operations
+
+- `Get(i, j)` / `Set(i, j, v)`: Access/update symmetric entries.
+- `Size() int`, `GetLabel(i)` / `SetLabel(i, label)`: Query/mutate element labels.
+- `Labels() []string`, `GetRow(i)` / `GetColumn(j)`: Retrieve full rows/columns (as copies).
+
+## Analysis Helpers
+
+- `MinDistance()`, `MaxDistance()` → `(value, i, j)` of the extremal off-diagonal entry.
+- `Copy() *DistMatrix`: Deep copy for immutability-safe operations.
+- `ToFullMatrix()` → `[][]float64`: Converts to dense representation (use sparingly).
+
+Designed for clustering, phylogenetics, or any domain requiring fast symmetric matrix access with minimal footprint.

autodoc/docmd/pkg/obidist/dist_matrix_test.md

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+# `obidist` Package: Semantic Feature Overview
+
+The `obidist` Go package provides two core data structures for managing **distance** and **similarity matrices**, with built-in guarantees suitable for scientific computing (e.g., clustering, phylogenetics). Key features include:
+
+- **`DistMatrix`**: A symmetric `n×n` matrix representing pairwise distances, where:
+  - Diagonal entries are *always* `0.0` (self-distance).
+  - Off-diagonals obey symmetry: `dist(i, j) == dist(j, i)`.
+  - Automatic enforcement via dedicated `Set()`/`Get()` methods.
+  
+- **`SimilarityMatrix`**: A symmetric matrix where:
+  - Diagonal entries are *always* `1.0`.
+  - Off-diagonals represent similarity scores (e.g., between `0` and `1`, though not enforced).
+  - Symmetry is similarly guaranteed.
+
+Both matrix types support:
+- **Optional labels**: Associate human-readable identifiers (e.g., sample names) with rows/columns.
+- **Safe bounds checking**: Panics on out-of-range access (tested via `defer/recover`).
+- **Deep copy support**: Ensures isolation between original and copied instances.
+- **Utility methods**:
+  - `MinDistance()` / `MaxDistance()`: Return extremal values and their indices.
+  - `GetRow(i)`: Retrieve a full row as a slice (symmetric copy).
+  - `ToFullMatrix()`: Export the matrix as an immutable 2D slice.
+
+Edge cases are rigorously handled:
+- Empty (`n=0`) and singleton (`n=1`) matrices return `(0.0, -1, -1)` for min/max.
+- Label mutations do not affect internal state via defensive copying.
+
+All behaviors are validated through comprehensive unit tests, emphasizing correctness and robustness.