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

let cop = Clayton::<f64>::with_seed(/* theta */ 2.0, 42);
let n = 10_000;
let (u, v) = cop.sample(n);                  // both Array1<f64> in [0, 1]

// Empirical Kendall's τ should be ≈ θ / (θ + 2) = 0.5
let tau_hat = stochastic_rs::stats::tail_index::kendall_tau(u.view(), v.view());
println!("τ̂ = {:.3}", tau_hat);

import stochastic_rs as srs

cop = srs.Clayton(theta=2.0, seed=42)
u, v = cop.sample(10_000)
print("Kendall's tau ≈", srs.kendall_tau(u, v))   # ≈ 0.5

use stochastic_rs::copulas::multivariate::gaussian::GaussianCopula;
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 = GaussianCopula::<f64>::with_seed(corr, 42);
let samples = cop.sample(10_000);            // Array2<f64>, shape (10_000, 3)

import stochastic_rs as srs
import numpy as np

corr = np.array([
    [1.0, 0.7, 0.3],
    [0.7, 1.0, 0.5],
    [0.3, 0.5, 1.0],
])
cop = srs.GaussianCopula(corr=corr, seed=42)
samples = cop.sample(10_000)                 # shape (10_000, 3)