T4 (MarginFactor): scored outcomes via Gaussian-margin EP evidence

Adds soft Gaussian-observation evidence on the per-pair diff variable,
enabling continuous score margins as a richer alternative to ranks.

Public API:
- `Outcome::Scored([scores])` (non-breaking enum extension under
  `#[non_exhaustive]`).
- `Game::scored(teams, outcome, options)` constructor parallel to
  `Game::ranked`.
- `EventBuilder::scores([...])` fluent helper.
- `HistoryBuilder::score_sigma(σ)` knob (default 1.0, validated > 0).
- `GameOptions::score_sigma`.
- `EventKind` re-exported from `lib.rs` (annotated `#[non_exhaustive]`).
- New `InferenceError::InvalidParameter { name, value }` variant.

Internals:
- `MarginFactor` (`factor/margin.rs`): Gaussian observation factor that
  closes in one EP step; cavity-cached log-evidence mirrors `TruncFactor`.
- `BuiltinFactor::Margin` dispatch arm.
- `DiffFactor` enum in `game.rs` lets `Game::likelihoods` and the new
  `likelihoods_scored` share the per-pair link abstraction.
- Per-event `EventKind { Ranked, Scored { score_sigma } }` routed through
  `TimeSlice::add_events`, `iteration_direct`, and `log_evidence`.

Tests: 88 lib + 27 integration (4 new in `tests/scored.rs`); existing
goldens byte-identical.  Bench: `benches/scored.rs` baseline ~960µs for
60 events × 20-player pool with default convergence.

Plan: docs/superpowers/plans/2026-04-27-t4-margin-factor.md
Spec item marked Done.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

benches/batch.rs

@@ -1,7 +1,7 @@
 use criterion::{Criterion, criterion_group, criterion_main};
 use trueskill_tt::{
-    BETA, Competitor, GAMMA, KeyTable, MU, P_DRAW, Rating, SIGMA, TimeSlice, drift::ConstantDrift,
-    gaussian::Gaussian, storage::CompetitorStore,
+    BETA, Competitor, EventKind, GAMMA, KeyTable, MU, P_DRAW, Rating, SIGMA, TimeSlice,
+    drift::ConstantDrift, gaussian::Gaussian, storage::CompetitorStore,
 };
 
 fn criterion_benchmark(criterion: &mut Criterion) {
@@ -33,8 +33,10 @@ fn criterion_benchmark(criterion: &mut Criterion) {
         weights.push(vec![vec![1.0], vec![1.0]]);
     }
 
+    let kinds = vec![EventKind::Ranked; composition.len()];
+
     let mut time_slice = TimeSlice::new(1, P_DRAW);
-    time_slice.add_events(composition, results, weights, &agents);
+    time_slice.add_events(composition, results, weights, kinds, &agents);
 
     criterion.bench_function("Batch::iteration", |b| {
         b.iter(|| time_slice.iteration(0, &agents))