//! Koopman operator theory for data-driven nonlinear control.
//!
//! This module provides tools for approximating the Koopman operator of a
//! nonlinear dynamical system and exploiting the resulting linear representation
//! for prediction and control.
//!
//! # Components
//!
//! - [`lifting_functions`] — Observable maps ψ: ℝᴺ → ℝᴸ (polynomial, RBF, delay embedding).
//! - [`edmd`] — Extended Dynamic Mode Decomposition (EDMD) to fit K from data.
//! - [`koopman_mpc`] — Single-step greedy Koopman MPC in the lifted space.
pub use Edmd;
pub use KoopmanGreedyMpc;
pub use ;