Search Results for "genstat statistical software"

A standard library for javascript and node.js. High performance, rigorous, and robust mathematical and statistical functions. Build advanced statistical models and machine learning libraries. Plotting and graphics functionality for data visualization and exploratory data analysis. Analyze and understand your data. Comprehensively tested utilities for application and library development. Functions to assert, group, filter, map, pluck, and transform your data both in browsers and on the...

Plotly JavaScript Open Source Graphing Library. Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform...

ARC-AGI is a benchmark dataset and experimental framework designed to evaluate and advance artificial general intelligence by testing systems on abstract reasoning tasks that require human-like problem-solving abilities. It consists of a curated set of tasks where models must infer patterns from input-output examples and apply those rules to new unseen cases, without relying on memorization or prior training data. The dataset is structured as grid-based puzzles, where each task requires...

Hiper is an open-source command-line tool designed for statistical analysis of web performance and page load behavior during performance testing. The tool repeatedly loads a specified webpage multiple times and gathers detailed timing metrics in order to produce more reliable performance measurements than single-run benchmarks. By averaging data across multiple page loads, Hiper helps developers understand whether performance optimizations actually improve real-world page loading behavior....

This library allows you to generate a random number in the specified range from 0 to n, without using the built-in function Math.random (), and gets a random number from the processing time of polymorphic mathematical calculations, which depends on the current physical parameters of the CPU, RAM, and t .P. The resulting sequence corresponds to a discrete uniform distribution and is close to natural random sequences in terms of statistical randomness tests (LFSR, Approximate Entropy, DIEHARD Test).