hast utility to get the rank (or depth, level) of headings
A standalone speech rule engine for XML structures, based on the original engine from ChromeVox.
Production-ready TypeScript data structures: Heap, Deque, Trie, Graph, Red-Black Tree, TreeMap, TreeSet, and more. Zero dependencies, type-safe, with getRank/getByRank/rangeByRank support.
Layered layout for directed acyclic graph
Simple dependency graph.
marshal: encoding and deconding of Passable subgraphs
MCP server that indexes codebases into an AST knowledge graph with semantic search, call graph traversal, and HTTP route tracing
Layered layout for directed acyclic graph
Runtime validation for static types
Manage real-time leaderboards using Redis
Get the graph of dependents in a monorepo
PageRank calculation for a ngraph.graph
Ranks and unranks permutations
Functional utility library - modern, simple, typed, powerful
Microsoft Graph Client Library
Ultra-fast MicroLoRA adaptation for WASM - rank-2 LoRA with <100us latency for per-operator learning
Beautiful and accessible math in all browsers. MathJax is an open-source JavaScript display engine for LaTeX, MathML, and AsciiMath notation that works in all browsers. This package includes the packaged components (install mathjax-full to get the source
WASM bindings for RuvLLM - browser-compatible LLM inference runtime with WebGPU acceleration
Example usage: ```javascript import { createClient, Graph } from 'redis';
rehype plugin that wraps section based on heading
2D force-directed graph rendered on HTML5 canvas
Parse sass files and extract a graph of imports
Beautiful and accessible math in all browsers. MathJax is an open-source JavaScript display engine for LaTeX, MathML, and AsciiMath notation that works in all browsers and in server-side node applications. This package includes the source code as well as
[](https://travis-ci.org/tmont/tarjan-graph) [](https://www.npmjs.com/package/tarjan-graph)
GraphRank is an impementation of TextRank and PageRank in Ruby.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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