regression

library0.1jsnpmunverified

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.

data ai-ml

npm install regression

github ↗package ↗

INSTALL

IMPORT

SIG · REGRESSION

regression

datajavascriptv0.1

Install

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Import

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Disk

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Pass rate

0/ 6

Env Coverage0 / 6

glibc

18–22

musl

18–22

Install & Compatibility

Where this runs

tested against v? · npm install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

node 18–226 runs

build_error

glibc

node 18–226 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

regression

✓ import regression from 'regression';

✗ const regression = require('regression');

The library primarily uses a default export pattern. The 'regression' package provides a single object containing all regression methods. While CommonJS `require` works in Node.js environments, ESM `import` is the recommended standard for modern JavaScript.

regression.linear

✓ import regression from 'regression'; const result = regression.linear(data);

✗ import { linear } from 'regression'; // Incorrect - linear is a method of the default export const result = linear(data);

Individual regression methods like `linear` are properties of the default `regression` object, not named exports themselves. They must be accessed via the imported default object.

regression.polynomial

✓ import regression from 'regression'; const result = regression.polynomial(data, { order: 3 });

✗ const result = regression('polynomial', data, 3); // Old API from earlier versions

Since version 2.0.0, the API for calling regression methods changed from a single `regression(type, data, options)` function to directly calling `regression.type(data, options)`. Also note the `order` option for polynomial regression.

Demonstrates how to import the `regression` library, perform linear and polynomial regression on sample data, and predict values using the generated models.

import regression from 'regression'; // Sample data: [[x1, y1], [x2, y2], ...] const data = [ [0, 1], [32, 67], [12, 79], [5, 10], [15, 30], [25, 50], [40, 85], [50, 100] ]; // Perform linear regression const linearResult = regression.linear(data); console.log('Linear Regression Result:'); console.log(' Equation:', linearResult.equation); // [m, c] console.log(' String:', linearResult.string); console.log(' R-squared (R²):', linearResult.r2); console.log(' Prediction for x=45:', linearResult.predict(45)[1]); // Perform polynomial regression with order 2 const polynomialResult = regression.polynomial(data, { order: 2 }); console.log('\nPolynomial Regression (Order 2) Result:'); console.log(' Equation:', polynomialResult.equation); // [a_n, ..., a_1, a_0] console.log(' String:', polynomialResult.string); console.log(' R-squared (R²):', polynomialResult.r2); console.log(' Prediction for x=45:', polynomialResult.predict(45)[1]);

Debug

Known issues

breakingVersion 2.0.0 introduced significant API changes, moving from a single `regression(type, data, options)` function to direct method calls like `regression.linear(data, options)`. Old call patterns will no longer work.

fix

Update usage to call specific regression methods directly on the imported `regression` object, e.g., `regression.linear(data)` instead of `regression('linear', data)`.

affects: >=2.0.0

gotchaInput data must be an array of `[x, y]` pairs (e.g., `[[0, 1], [32, 67]]`). Providing data in other formats (e.g., objects like `{ x: 0, y: 1 }` or separate X and Y arrays) will lead to errors.

fix

Ensure all input data is formatted as `Array<[number, number]>`.

affects: >=1.0.0

gotchaThe `precision` option (defaulting to 2 significant figures) rounds the output equation coefficients. If high precision is required for intermediate calculations, be aware of this rounding or set `precision` to a higher value or `null`.

fix

Set the `precision` option in the configuration object to a higher number or `null` for full precision, e.g., `{ precision: 10 }` or `{ precision: null }`.

affects: >=1.0.0

deprecatedThe `lastvalue` and `linearThroughOrigin` methods were removed in version 2.0.0. Linear regression through the origin is now handled by the standard `linear` model.

fix

For linear regression through the origin, use `regression.linear()` and interpret the `yIntercept` as zero if that is your model assumption. The `lastvalue` functionality is no longer available directly.

affects: >=2.0.0

gotchaThe package's last publish was over 8 years ago. While stable, this indicates that the library is in maintenance mode and unlikely to receive new features, active bug fixes, or updates for modern JavaScript language features or security concerns.

fix

Consider its long-term viability for new projects. For active development, assess if an actively maintained alternative might be more suitable, or be prepared to fork and maintain if specific updates are needed.

affects: *

Errors

Common errors & fixes

TypeError: regression.linear is not a function

Attempting to call a method on `regression` when it was imported incorrectly, or the `regression` object itself is undefined due to a CJS/ESM mixup.

fix

Ensure you are using `import regression from 'regression';` for ESM contexts, or `const regression = require('regression');` for CommonJS. If using `regression('linear', data)`, update to `regression.linear(data)` as the old API was removed in v2.0.0.

TypeError: data is not an array

The `data` argument passed to any regression method is not in the expected `[[x1, y1], [x2, y2], ...]` format.

fix

Verify that your input data is an array of arrays, where each inner array contains exactly two numbers representing an [x, y] coordinate. E.g., `[[1, 2], [3, 4], [5, 6]]`.

TypeError: Cannot read properties of undefined (reading 'equation')

This usually occurs when trying to access properties like `equation`, `string`, or `r2` from the result of a regression call that failed or returned an unexpected value (e.g., if `data` was malformed and the function returned `undefined`).

fix

Before accessing properties, check if the `result` object is valid and contains the expected properties. Ensure the input `data` format is correct and the regression function executed successfully.

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Version history

0.1latest on npm

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Resources

githubhttps://github.com/tom-alexander/regression-js ↗

packagehttps://www.npmjs.com/package/regression ↗