$ npm install r-regression --save
Regression transform for Vega dataflows.
Calculate statistical regressions for two-dimensional data
Simple Linear Regression
Javascript least squares data fitting methods
Base class for regression modules
Module for adding visual regression testing to Cypress
This allows you to add regression lines to any series. Supports: linear, polynomial, logarithmic, exponential and loess. Calculates the r-value
Polynomial Regression
Theil-Sen regression
Power regression
Exponential Regression
Multivariate linear regression
Regression algorithms
Robust polynomial regression using LMedS
Logistic regression
Polynomial Regression 2D
TypeScript definitions for regression
JavaScript SDK for Visual Regression Tracker
Native integration for Playwright with Visual Regression Tracker
Spatial Regression module for GeoDaLib
Command-line interface for mermaid
Visual regression testing CLI, Wasm-backed. Drop-in compatible with classic reg-cli's CLI flags, reg.json/junit schema, and `compare()` EventEmitter API (verified against reg-suit's processor.ts).
Visual Regression Testing for Storybook
A suite for basic and advanced statistics on Ruby. Tested on CRuby 1.9.3, 2.0.0 and 2.1.1. See `.travis.yml` for more information. Include: - Descriptive statistics: frequencies, median, mean, standard error, skew, kurtosis (and many others). - Correlations: Pearson's r, Spearman's rank correlation (rho), point biserial, tau a, tau b and gamma. Tetrachoric and Polychoric correlation provides by statsample-bivariate-extension gem. - Intra-class correlation - Anova: generic and vector-based One-way ANOVA and Two-way ANOVA, with contrasts for One-way ANOVA. - Tests: F, T, Levene, U-Mannwhitney. - Regression: Simple, Multiple (OLS), Probit and Logit - Factorial Analysis: Extraction (PCA and Principal Axis), Rotation (Varimax, Equimax, Quartimax) and Parallel Analysis and Velicer's MAP test, for estimation of number of factors. - Reliability analysis for simple scale and a DSL to easily analyze multiple scales using factor analysis and correlations, if you want it. - Dominance Analysis, with multivariate dependent and bootstrap (Azen & Budescu) - Sample calculation related formulas - Structural Equation Modeling (SEM), using R libraries +sem+ and +OpenMx+ - Creates reports on text, html and rtf, using ReportBuilder gem - Graphics: Histogram, Boxplot and Scatterplot.
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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