What’s New in 0.12

Statistics, redesigned to compose. A distribution is now a Family — a struct with its support, density and closed forms — behind an opaque Distribution handle that owns the generic machinery. That is what makes the new constructions one-liners:

• x.given(&event) — conditioning (Truncated): E[N | N > 0] = √(2/π), E[B | B ≥ 2] = 325/131.
• x.transform("Y", &g) — aX + b with every closed form transported (Affine), strictly monotone maps and X²/|X| by the change-of-variables formula (Transformed), finite ranges mapped and merged.
• Distribution::mixture(&[(w, F), …]).
• Your own families: implement Family, wrap with Distribution::from_family.

Events through the set machinery. With numeric bounds any boolean combination of relations in the variable is accepted — P(N² < 1), P(N < −1 ∨ N > 1), E[X | X² > 1] — and several 0.11 answers that were wrong are fixed (P(X = 3 ∧ X > 5), P(X > 1 ∧ X ≥ 2), P(X = ½) for an integer variable, Uniform(0,1).cdf(3)).

New closed forms. betainc / betainc_regularized (SymPy’s 4-argument form) give Beta, StudentT and the new FDistribution their CDFs; erfinv compiles, so Normal/LogNormal (and everything built on them) sample; Σ C(k+c, k) xᵏ and the binomial theorem with symbolic n and p close, so NegativeBinomial needs no polynomial workaround.

See Migrating from 0.11 to 0.12 for the one-line fixes to code that matched on the old enums.