Implement SNP distance models with gamma correction and PHYLIP output

Renames the CLI flag from --metric to --distance and introduces eight closed-form SNP distance models with optional Jin-Nei gamma correction. Integrates the ndarray crate for matrix operations and adds relaxed PHYLIP output formatting. Updates architecture and theory documentation to cover the new sparse matrix variants, algorithmic fixes, and distance metric implementations.

DevDoc/implementation/obicompactvec/index.html

@@ -1168,7 +1168,7 @@ Pass 1 — byte max, SIMD-vectorizable, O(n)
 </code></pre></div>
 <hr/>
 <h2 id="matrix-types">Matrix types</h2>
-<p>Both matrix types are enums behind a transparent API — the caller never matches on the variant. <code>PersistentCompactIntMatrix</code> has two variants (<code>Columnar</code>, <code>Packed</code>). <code>PersistentBitMatrix</code> has four:</p>
+<p>Both matrix types are enums behind a transparent API — the caller never matches on the variant. <code>PersistentCompactIntMatrix</code> has three variants (<code>Columnar</code>, <code>Packed</code>, <code>Sparse</code>). <code>PersistentBitMatrix</code> has four:</p>
 <table>
 <thead>
 <tr>
@@ -1185,12 +1185,12 @@ Pass 1 — byte max, SIMD-vectorizable, O(n)
 </tr>
 <tr>
 <td><code>Packed</code></td>
-<td>single <code>matrix.pbmx</code> mmap file</td>
+<td>single <code>matrix.pbmx</code>/<code>matrix.pcmx</code> mmap file</td>
 <td>query-optimised, produced by <code>pack_bit_matrix</code>/<code>pack_compact_int_matrix</code></td>
 </tr>
 <tr>
-<td><code>Sparse</code> (bit only)</td>
-<td><code>sparse_meta.json</code> + PFIV/Elias-Fano component files, row-major</td>
+<td><code>Sparse</code></td>
+<td>bit: <code>sparse_meta.json</code> + PFIV/Elias-Fano component files, row-major. Int: same support files (built on <code>PersistentSparseBitMatrix</code> internally) plus <code>singleton_values.pciv</code>/<code>multi_values.pciv</code>/<code>multi_offsets</code> for the per-row, non-deduplicated values</td>
 <td><code>pack --sparse</code>; see <a href="../../architecture/siblings/">siblings.md</a> for the sparse-vs-dense access-pattern trade-off</td>
 </tr>
 <tr>
@@ -1200,7 +1200,8 @@ Pass 1 — byte max, SIMD-vectorizable, O(n)
 </tr>
 </tbody>
 </table>
-<p><code>PersistentBitMatrix::open(layer_dir)</code> auto-detects the variant, in order: <code>matrix.pbmx</code> → Packed, <code>presence/meta.json</code> → Columnar, <code>presence/sparse_meta.json</code> → Sparse, <code>layer_meta.json</code> (no presence dir at all) → Implicit. <code>col_view</code>/<code>col</code>/<code>sub_matrix</code> panic on <code>Sparse</code>/<code>Implicit</code> where the operation has no direct-slice equivalent (Sparse is k-mer-major, not column-major; Implicit has no backing storage) — callers needing per-column data on those variants go through <code>row</code>/<code>fill_row</code>.</p>
+<p><code>PersistentBitMatrix::open(layer_dir)</code> auto-detects the variant, in order: <code>matrix.pbmx</code> → Packed, <code>presence/meta.json</code> → Columnar, <code>presence/sparse_meta.json</code> → Sparse, <code>layer_meta.json</code> (no presence dir at all) → Implicit. <code>PersistentCompactIntMatrix::open(layer_dir)</code> mirrors the same priority order minus <code>Implicit</code> (there's no implicit count matrix — counts always have at least one on-disk column): <code>matrix.pcmx</code> → Packed, <code>counts/meta.json</code> → Columnar, <code>counts/singleton_values.pciv</code> → Sparse. <code>col_view</code>/<code>col</code>/<code>sub_matrix</code> panic on <code>Sparse</code>/<code>Implicit</code> where the operation has no direct-slice equivalent (Sparse is k-mer-major, not column-major; Implicit has no backing storage) — callers needing per-column data on those variants go through <code>row</code>/<code>fill_row</code>.</p>
+<p>Unlike the bit side, <code>PersistentSparseCompactIntMatrix</code>'s values are <em>not</em> deduplicated across rows — two rows can share the same non-zero column set (same <code>dict_id</code> in the shared support) while carrying different counts — so its <code>CountPartials</code> impl can't reuse the support's dict-multiplicity shortcut the way <code>BitPartials for PersistentSparseBitMatrix</code> does. It still avoids the naive <code>O(n_cols² × n)</code> column-pair scan via a single row-major pass (<code>row_major_pairwise</code> in <code>sparse_intmatrix.rs</code>), reconstructing the squared-difference formulas (<code>euclidean</code>/<code>relfreq_euclidean</code>/<code>hellinger</code>) from per-column marginals via <code>Σ(a-b)² = Σa²+Σb²-2Σab</code> — see <a href="../../architecture/siblings/">siblings.md</a>'s "<code>PersistentCompactIntMatrix::Sparse</code> — implemented" entry for the full derivation.</p>
 <p><code>col_view(c)</code> returns the appropriate view directly:</p>
 <div class="highlight"><pre><span></span><code><span class="c1">// PersistentBitMatrix</span>
 <span class="k">pub</span><span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">col_view</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">c</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">BitSliceView</span><span class="o">&lt;'</span><span class="nb">_</span><span class="o">&gt;</span>