A tool set for CSS: fast detailed parser (CSS → AST), walker (AST traversal), generator (AST → CSS) and lexer (validation and matching) based on specs and browser implementations
RustCrypto: Argon2 binding for Node.js
Node.js atomic and non-atomic counters, rate limiting tools, protection from DoS and brute-force attacks at scale
Strip comments from JSON. Lets you use comments in your JSON files!
Neo-Async is a drop-in replacement for Async, it almost fully covers its functionality and runs faster
Intl.LocaleMatcher ponyfill
Specialized fast async file writer
A fast alternative to legacy querystring module
High-performance Base64 encoder and decoder
node-simple-lru-cache =====================
Escape string for use in HTML
🔎 A simple, tiny and lightweight benchmarking library!
LongMemEval benchmark implementation for Mastra Memory
A simple MD5 hash function for JavaScript supports UTF-8 encoding.
A node API for the dprint TypeScript and JavaScript code formatter
Fastest, most accurate & effecient user agent string parser, uses Browserscope's research for parsing
The lightest signal library.
The most efficient JS implementation calculating the Levenshtein distance, i.e. the difference between two strings.
Faster swc nodejs binding
Classify GPU's based on their benchmark score in order to provide an adaptive experience.
The most efficient JS implementation calculating the Levenshtein distance, i.e. the difference between two strings.
<xml for="JavaScript">
TypeScript definitions for benchmark
Benchmarking tools
Benchmark-style memory profiling
Process memory and cpu time benchmarker
Benchmark Garbage Collect stats relevant to memory optimization.
Quickly benchmark execution time and profile memory allocations for specific or all instance methods within a class. Include ClassProfiler to get `benchmark_methods` and `profile_methods` helpers and collect results via `benchmarked` and `profiled_memory`.
Benchmark Sweet is a suite to run multiple kinds of metrics that allows for the generation of complex comparisons. It can be configured to run memory, sql query, and ips benchmarks on a common set of code. These data can be collected across multiple runs with numerous supported ruby or gem versions.
IrtRuby is a comprehensive Ruby library for Item Response Theory (IRT) analysis, commonly used in educational assessment, psychological testing, and survey research. Features three core IRT models: • Rasch Model (1PL) - Simple difficulty-only model • Two-Parameter Model (2PL) - Adds item discrimination • Three-Parameter Model (3PL) - Includes guessing parameter Key capabilities: • Robust gradient ascent optimization with adaptive learning rates • Flexible missing data strategies (ignore, treat as incorrect/correct) • Comprehensive performance benchmarking suite • Memory-efficient implementation with excellent scaling • Production-ready with extensive test coverage Perfect for researchers, data scientists, and developers working with educational assessments, psychological measurements, or any binary response data where item and person parameters need to be estimated simultaneously.
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