feat: add fANOVA parameter importance via random forest

Implement functional ANOVA decomposition behind the `fanova` feature
flag. A self-contained random forest is trained on trial data, then
marginal predictions are used to compute per-parameter main effects
and pairwise interaction effects, normalized to sum to 1.0.

Adds Study::fanova() / fanova_with_config(), FanovaResult, and
FanovaConfig. Includes unit and integration tests covering dominant
parameters, interaction detection, consistency with correlation-based
importance, and error handling.

src/lib.rs

@@ -219,6 +219,8 @@ macro_rules! trace_debug {
 
 mod distribution;
 mod error;
+#[cfg(feature = "fanova")]
+mod fanova;
 mod importance;
 mod kde;
 pub mod multi_objective;
@@ -234,6 +236,8 @@ mod types;
 mod visualization;
 
 pub use error::{Error, Result, TrialPruned};
+#[cfg(feature = "fanova")]
+pub use fanova::{FanovaConfig, FanovaResult};
 pub use multi_objective::{MultiObjectiveSampler, MultiObjectiveStudy, MultiObjectiveTrial};
 #[cfg(feature = "derive")]
 pub use optimizer_derive::Categorical;
@@ -274,6 +278,8 @@ pub mod prelude {
     pub use optimizer_derive::Categorical as DeriveCategory;
 
     pub use crate::error::{Error, Result, TrialPruned};
+    #[cfg(feature = "fanova")]
+    pub use crate::fanova::{FanovaConfig, FanovaResult};
     pub use crate::multi_objective::{MultiObjectiveStudy, MultiObjectiveTrial};
     pub use crate::param::ParamValue;
     pub use crate::parameter::{