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R package, using vis.js library for network visualization
The main features of datastorm-open/visnetwork are: Network Analysis, Network Visualization, Html Widgets.
Projects with overlapping indexed features include: vincentgarreau/particles.js — particles.js is a JavaScript particle animation library and visual effects engine used to create interactive,… leaverou/awesomplete — Awesomplete is a lightweight JavaScript library for adding searchable suggestion lists to text fields. It functions as… clips/pattern — Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit,… christophergandrud/networkd3 — Version 0.2.3. cytoscape/cyjshiny — An R/shiny widget for cytoscape.js. cynkra/g6r — experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/sta…
Awesomplete is a lightweight JavaScript library for adding searchable suggestion lists to text fields. It functions as an HTML-based autocomplete widget that renders filtered results from arrays, HTML elements, or comma-separated strings with zero external dependencies. The library includes a customizable suggestion engine that allows for the definition of custom matching logic, sorting rules, and trigger thresholds. It is designed as an event-driven UI component, providing a programmable interface to intercept selection events and manage the state of the suggestion popup. The system support
particles.js is a JavaScript particle animation library and visual effects engine used to create interactive, canvas-based simulations for web interfaces. It enables the generation of dynamic background animations and networked visual effects through coordinate-based particle movements. The library features a system for drawing connecting lines between particles based on proximity to create network-style visualizations. It includes mechanisms for mapping mouse movements and click events to specific particle behaviors, such as attraction or repulsion. Users can configure particle density, opa
Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit, and a network analysis tool. It provides a mathematical framework for categorizing datasets through a vector space model library. The project enables the extraction of structured data from web services and the creation of searchable web content indexes. It processes unstructured text using sentiment analysis, part-of-speech tagging, and n-gram searching. The library covers machine learning classification through the training of models using perceptron algorithms and support ve
