Simple Linear Regression
nodejs module for calculating a simple regression line from bivariate data using the 'linear least squares' function
Simple Linear Regression
Simple Linear Regression
Simple linear regression calculator
Calculate statistical regressions for two-dimensional data
Regression algorithms
Multivariate linear regression
Javascript least squares data fitting methods
Perform simple linear regression using Ordinary Least Squares (OLS)
A <LinearGradient> element for React Native
This allows you to add regression lines to any series. Supports: linear, polynomial, logarithmic, exponential and loess. Calculates the r-value
Regression transform for Vega dataflows.
The Linear Client SDK for interacting with the Linear GraphQL API
GPU Javascript Library for Machine Learning
Use the display-p3-linear color space on the color() function in CSS
Fit a model to noisy data by excluding outliers. This is an implementation of the RANSAC algorithm.
Statistical routines and probability distributions.
Module for adding visual regression testing to Cypress
Provides a React component that renders a gradient view.
Base class for regression modules
A library to find JS RegExp with super-linear worst-case time complexity for attack strings that repeat a single character.
The Material Components for the web linear progress indicator component
Power regression
A simple library for calculating linear trend regressions against a time series data set. See README for more info
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
This is a simple machine learning library written in Ruby. It provides implementations of linear regression and multiclass perceptron and visualization and validation methods to verify results. Also included are helper methods to work with training and testing data.
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
Ruby Scientist and Graphics is a practical data science toolkit for Ruby. It includes a lightweight built-in DataFrame for loading, cleaning, and transforming data; quick descriptive statistics and correlations; charting via Gruff (bar and line); and simple ML utilities (linear regression and k-means)—all behind a small, unified, pandas-inspired API. Key features: - Load data from CSV and JSON. - Clean and transform (remove/add columns, handle missing values, limit rows). - Describe datasets and compute correlations quickly. - Create bar and line charts with customization options. - Train/predict with linear regression; cluster with k-means. - Save/load project state (data + trained model) and run simple pipelines. - Optional backend adapters (e.g., Rover) while keeping the same API. Ideal for analysts and developers who want to explore data in Ruby without relying on Python or R. Note: plotting via Gruff uses rmagick, which requires ImageMagick installed on the system.
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