use stochastic_rs::copulas::bivariate::clayton::Clayton;
use stochastic_rs::traits::BivariateExt;

let mut cop = Clayton::new();
cop.set_tau(0.5);                            // τ ⇒ θ via Kendall inversion
let _ = cop.compute_theta();
let uv = cop.sample_with_seed(10_000, 42)?;  // Array2<f64>, shape (10_000, 2)
let (u, v) = (uv.column(0), uv.column(1));   // both in [0, 1]

let tau_hat = stochastic_rs::stats::tail_index::kendall_tau(u, v);
println!("τ̂ = {:.3}", tau_hat);

import stochastic_rs as srs

cop = srs.Clayton(theta=2.0)
uv = cop.sample(10_000, seed=42)             # shape (10_000, 2)
u, v = uv[:, 0], uv[:, 1]
print("Kendall's tau ≈", srs.kendall_tau(u, v))   # ≈ 0.5

use stochastic_rs::copulas::multivariate::gaussian::GaussianMultivariate;
use stochastic_rs::traits::MultivariateExt;
use ndarray::array;

let corr = array![
    [1.0, 0.7, 0.3],
    [0.7, 1.0, 0.5],
    [0.3, 0.5, 1.0],
];
let cop = GaussianMultivariate::new_with_corr(corr)?;
let samples = cop.sample(10_000)?;           // Array2<f64>, shape (10_000, 3)