feat: add the missing trait impls and make #[must_use] consistent

Trait coverage (#76), all additive:

  History          Debug is still absent - see below
  HistoryBuilder   + Debug   (it derived Clone but not Debug)
  Rating           + PartialEq  (Gaussian had it; Rating is a Gaussian
                                 plus three scalars and had none)
  Event/Team/Member + PartialEq (input value types with no way to compare
                                 them, which made round-trip tests awkward)
  ConvergenceReport + PartialEq

`#[must_use]` (#67). The coverage had no rule: `filtered_log_evidence`
had it and `log_evidence` did not; `rating` had it and `current_skill`
did not; `Rating::with_drift_scale` had it and `Member::with_drift_scale`
did not.

Now on the types — `EventBuilder`, `HistoryBuilder`, `Prediction`,
`Gaussian`, `OwnedGame` — which covers most method returns at once, plus
the `History` accessors individually.

`EventBuilder` gets a message, because a dropped builder is the worst
case in the set: measured, `h.event(1).team(["x"]).team(["y"]).winner(0)`
without `.commit()` leaves `time_slices_len() == 0` and every skill
`None`, with no warning at all.

And `ConvergenceReport`'s `#[must_use]` moves off the TYPE onto
`converge_partial`, where its stated reason is true. It read "from
`converge_partial` this may describe a fit that stopped at max_iter" but
fired on `converge` too — where that is false, since `converge` returns
`Err(NotConverged)` in exactly that case. So the crate's own front-page
example warned, and every quickstart had to write `let _ =`. Verified
from a consumer crate: `h.converge()?;` now compiles clean.

Marking the types made eight method-level attributes redundant, which
clippy's `double_must_use` caught — that is the type-level marker doing
its job, and the eight are removed.

Refs #76, #67

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ

src/predict.rs

@@ -456,6 +456,7 @@ pub(crate) fn ranking_probability(
 /// `Game::ranked` asks "what would we believe if *this* happened", which is
 /// what an expected-information-gain calculation needs alongside the weight.
 #[derive(Clone, Debug, PartialEq)]
+#[must_use]
 pub struct Prediction {
     outcomes: Vec<(Vec<u32>, f64)>,
 }