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>Basic OLS — 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>Basic OLS Regression — linreg-core WASM</h1>
  <p>Fits an OLS model: <code>price ~ sqft + bedrooms</code></p>
  <pre id="output">Loading WASM…</pre>

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

    const y        = [150, 200, 250, 300, 220, 270, 310, 180, 240, 290];
    const sqft     = [ 10,  14,  18,  22,  15,  19,  23,  12,  17,  21];
    const bedrooms = [  2,   3,   3,   4,   3,   3,   4,   2,   3,   4];
    const names    = ['Intercept', 'SqFt', 'Bedrooms'];

    const r = JSON.parse(ols_regression(
      JSON.stringify(y),
      JSON.stringify([sqft, bedrooms]),
      JSON.stringify(names)
    ));

    const sig = p => p < 0.001 ? '***' : p < 0.01 ? '**' : p < 0.05 ? '*' : '';
    const f4  = v => v.toFixed(4);

    const lines = [
      '── Coefficients ───────────────────────────────────────────',
      'Variable         Coef         SE            t         p',
      '─'.repeat(58),
    ];
    names.forEach((name, i) => {
      lines.push(
        name.padEnd(14) +
        f4(r.coefficients[i]).padStart(12) + '  ' +
        f4(r.std_errors[i]).padStart(10) + '  ' +
        r.t_stats[i].toFixed(3).padStart(8) + '  ' +
        r.p_values[i].toExponential(2).padStart(10) + '  ' +
        sig(r.p_values[i])
      );
    });

    lines.push('');
    lines.push('── Model Fit ──────────────────────────────────────────────');
    lines.push('R²:           ' + f4(r.r_squared));
    lines.push('Adj R²:       ' + f4(r.adj_r_squared));
    lines.push('F-statistic:  ' + r.f_statistic.toFixed(2) + '  (p = ' + r.f_p_value.toExponential(3) + ')');
    lines.push('MSE:          ' + f4(r.mse));
    lines.push('Observations: ' + r.n);
    lines.push('Predictors:   ' + r.k);
    lines.push('DF residual:  ' + r.df);

    lines.push('');
    lines.push('── VIF (multicollinearity) ────────────────────────────────');
    r.vif.forEach(v => {
      const note = v.vif > 10 ? ' (HIGH)' : v.vif > 5 ? ' (moderate)' : ' (ok)';
      lines.push('  ' + v.variable.padEnd(12) + ' VIF = ' + f4(v.vif) + '  ' + v.interpretation);
    });

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