{"library":"regression","title":"Regression-js: Least Squares Data Fitting","description":"regression-js is a JavaScript module that provides a collection of linear least-squares fitting methods for simple data analysis. It offers capabilities for linear, exponential, logarithmic, power, and polynomial regression. The current stable version is 2.0.1, last published over 8 years ago, suggesting a mature but potentially unmaintained codebase, though it remains widely used. It is a lightweight, pure JavaScript solution that runs both in Node.js and modern browsers. Unlike some broader machine learning libraries, regression-js focuses specifically on classical least-squares curve fitting, providing a straightforward API for common trend analysis tasks without external dependencies.","language":"javascript","status":"maintenance","last_verified":"Sun Apr 19","install":{"commands":["npm install regression"],"cli":null},"imports":["import regression from 'regression';","import regression from 'regression'; const result = regression.linear(data);","import regression from 'regression'; const result = regression.polynomial(data, { order: 3 });"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import regression from 'regression';\n\n// Sample data: [[x1, y1], [x2, y2], ...]\nconst data = [\n  [0, 1],\n  [32, 67],\n  [12, 79],\n  [5, 10],\n  [15, 30],\n  [25, 50],\n  [40, 85],\n  [50, 100]\n];\n\n// Perform linear regression\nconst linearResult = regression.linear(data);\nconsole.log('Linear Regression Result:');\nconsole.log('  Equation:', linearResult.equation); // [m, c]\nconsole.log('  String:', linearResult.string);\nconsole.log('  R-squared (R²):', linearResult.r2);\nconsole.log('  Prediction for x=45:', linearResult.predict(45)[1]);\n\n// Perform polynomial regression with order 2\nconst polynomialResult = regression.polynomial(data, { order: 2 });\nconsole.log('\\nPolynomial Regression (Order 2) Result:');\nconsole.log('  Equation:', polynomialResult.equation); // [a_n, ..., a_1, a_0]\nconsole.log('  String:', polynomialResult.string);\nconsole.log('  R-squared (R²):', polynomialResult.r2);\nconsole.log('  Prediction for x=45:', polynomialResult.predict(45)[1]);","lang":"javascript","description":"Demonstrates how to import the `regression` library, perform linear and polynomial regression on sample data, and predict values using the generated models.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}