A JavaScript implementation of the FREQUENT algorithm for identifying frequent items in a data stream in sliding window
JWA implementation (supports all JWS algorithms)
Implementation of JSON Web Signatures
iOS & Android BackgroundFetch API implementation for React Native
Detect the indentation of code
Sniff the encoding from a HTML byte stream
Simple “Least Recently Used” (LRU) cache
Efficient implementation of Levenshtein algorithm with locale-specific collator support.
The fastest and smallest JavaScript polygon triangulation library for your WebGL apps
Fast 2D concave hull algorithm in JavaScript (generates an outline of a point set)
The lightest signal library.
An implementation of the Unicode Line Breaking Algorithm (UAX #14)
The fCoSE layout for Cytoscape.js by Bilkent with fast compound node placement
Parse a JSON string that has git merge conflicts, resolving if possible
Hashing made simple. Get the hash of a buffer/string/stream/file.
A JS library for finding optimal label position inside a polygon
SHA1 wrapper for browsers that prefers `window.crypto.subtle`.
A point in polygon based on the paper Optimal Reliable Point-in-Polygon Test and Differential Coding Boolean Operations on Polygons
Martinez polygon clipping algorithm, does boolean operation on polygons (multipolygons, polygons with holes etc): intersection, union, difference, xor
Periodic callbacks in the background for both IOS and Android
graph algorithm
Text hyphenation in Javascript.
Hardhat is an extensible developer tool that helps smart contract developers increase productivity by reliably bringing together the tools they want.
Find the longest common subsequence.
frequent-algorithm is a Ruby implementation of the Demaine et al FREQUENT algorithm for identifying frequent items in a data stream in sliding windows (c.f Identifying Frequent Items in Sliding Windows over On-Line Packet Streams, 2003).
This is an implementation of the fp-growth frequent pattern mining algorithm as stated in the paper Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach Han et al, Data Mining and Knowledge Discovery, 8, 53-87, 2004
A self-balancing binary tree optimised for fast access to frequently used nodes. Useful for implementing caches and garbage collection algorithms.
Inflect English nouns and verbs. The algorithms are based on the analysis of 7,000 most frequently used nouns and 6,000 most used verbs in English language.
Huffify is a Ruby gem that provides functionality to encode and decode text data using Huffman encoding, a lossless data compression algorithm. Huffman encoding efficiently compresses data by assigning shorter codes to more frequent symbols and longer codes to less frequent symbols.