linreg-core 0.8.1

Lightweight regression library (OLS, Ridge, Lasso, Elastic Net, WLS, LOESS, Polynomial) with 14 diagnostic tests, cross validation, and prediction intervals. Pure Rust - no external math dependencies. WASM, Python, FFI, and Excel XLL bindings.
Documentation 
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<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Regularized Regression — linreg-core WASM</title>
  <style>
    body { font-family: monospace; max-width: 760px; margin: 2rem auto; padding: 0 1rem; }
    h1   { font-size: 1.3rem; border-bottom: 2px solid #333; padding-bottom: 0.3rem; }
    pre  { background: #f5f5f5; padding: 1rem; border-radius: 4px; }
  </style>
</head>
<body>
  <h1>Regularized Regression — linreg-core WASM</h1>
  <p>Ridge, Lasso, and Elastic Net on collinear data. OLS shown for comparison.</p>
  <pre id="output">Loading WASM…</pre>

  <script type="module">
    import init, {
      ols_regression,
      ridge_regression,
      lasso_regression,
      elastic_net_regression,
    } from 'https://unpkg.com/linreg-core/linreg_core.js';
    await init();

    // Collinear dataset: x2 ≈ x1 + small noise
    const y  = [2.1, 3.8, 5.5, 7.2, 9.0, 10.7, 12.4, 14.1, 15.8, 17.5];
    const x1 = [1,   2,   3,   4,   5,   6,    7,    8,    9,    10];
    const x2 = [1.1, 2.2, 2.9, 4.1, 5.0, 6.2,  6.8,  8.1,  9.2,  9.9];

    const yJson    = JSON.stringify(y);
    const xJson    = JSON.stringify([x1, x2]);
    const namesJson = JSON.stringify(['Intercept', 'X1', 'X2']);

    const ols   = JSON.parse(ols_regression(yJson, xJson, namesJson));
    const ridge = JSON.parse(ridge_regression(yJson, xJson, namesJson, 1.0, true));
    const lasso = JSON.parse(lasso_regression(yJson, xJson, namesJson, 0.5, true, 10000, 1e-7));
    const enet  = JSON.parse(elastic_net_regression(yJson, xJson, namesJson, 0.5, 0.5, true, 10000, 1e-7));

    console.log('ridge', ridge, 'lasso', lasso, 'enet', enet);

    const f4 = v => (v != null ? v.toFixed(4) : '—');

    // OLS: coefficients[0]=intercept, [1..]=predictors
    // Ridge/Lasso/ENet: intercept separate, coefficients[0..]=predictors
    const olsCoefs   = ols.coefficients;
    const ridgeCoefs = [ridge.intercept, ...ridge.coefficients];
    const lassoCoefs = [lasso.intercept, ...lasso.coefficients];
    const enetCoefs  = [enet.intercept,  ...enet.coefficients];

    const lines = [];
    lines.push('── Coefficients ─────────────────────────────────────────────────────');
    lines.push('Variable         OLS      Ridge(λ=1)  Lasso(λ=0.5)  ENet(λ=0.5,α=0.5)');
    lines.push('─'.repeat(70));
    ['Intercept', 'X1', 'X2'].forEach((name, i) => {
      lines.push(
        name.padEnd(16) +
        f4(olsCoefs[i]).padStart(9)   + '  ' +
        f4(ridgeCoefs[i]).padStart(10) + '  ' +
        f4(lassoCoefs[i]).padStart(12) + '  ' +
        f4(enetCoefs[i]).padStart(13)
      );
    });

    lines.push('');
    lines.push('── Model Fit ────────────────────────────────────────────────────────');
    lines.push('Metric           OLS      Ridge       Lasso         ENet');
    lines.push('─'.repeat(60));
    lines.push(
      'R²'.padEnd(16) +
      f4(ols.r_squared).padStart(9)   + '  ' +
      f4(ridge.r_squared).padStart(10) + '  ' +
      f4(lasso.r_squared).padStart(12) + '  ' +
      f4(enet.r_squared).padStart(13)
    );
    lines.push(
      'MSE'.padEnd(16) +
      f4(ols.mse).padStart(9)   + '  ' +
      f4(ridge.mse).padStart(10) + '  ' +
      f4(lasso.mse).padStart(12) + '  ' +
      f4(enet.mse).padStart(13)
    );

    lines.push('');
    lines.push('── Lasso / Elastic Net Sparsity ─────────────────────────────────────');
    lines.push('  Lasso  non-zero: ' + lasso.n_nonzero + '  converged: ' + lasso.converged);
    lines.push('  ENet   non-zero: ' + enet.n_nonzero  + '  converged: ' + enet.converged);
    lines.push('');
    lines.push('  Note: regularization shrinks collinear coefficients toward zero.');
    lines.push('  Lasso can zero out redundant predictors entirely.');

    document.getElementById('output').textContent = lines.join('\n');
  </script>
</body>
</html>