Statistics
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A comprehensive, dependency-free statistics library for Go with extensive mathematical functions and thorough testing.
A comprehensive collection of machine learning algorithms and mathematical utilities implemented in JavaScript for browser and Node.js.
A Rack-based A/B testing framework for Ruby web applications, designed to work with Rails, Sinatra, or any Rack-based app.
A pure Python library for survival analysis, modeling time-to-event data with censoring.
HyperLearn provides 2-2000x faster machine learning algorithms with 50% less memory usage, optimized for all hardware.
A comprehensive, self-contained mathematics library for PHP with no external dependencies, covering algebra, statistics, linear algebra, and numerical analysis.
A concise mathematical reference covering essential topics in probability theory and statistics.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
A curated collection of R tutorials, packages, and resources for Data Science, NLP, and Machine Learning.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A flexible and fast package for in-memory tabular data manipulation and analysis in the Julia programming language.
A Ruby gem that benchmarks code performance in iterations per second with automatic iteration scaling.
A self-hosted, open-source dashboard for visualizing and analyzing personal Strava activity data.
A self-hosted music scrobble database for creating personal listening statistics and charts.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A Go machine learning library with online learning capabilities and a variety of implemented models.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A collection of programming articles covering C++, Elm, Haskell, Kotlin, statistics, and software development concepts.
An easy and extensible benchmarking library for Elixir that provides comprehensive statistics and memory measurements.
A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
A suite of high-performance command line tools for filtering, summarizing, joining, and manipulating large tabular data files.
A curated collection of 500+ resources for data analysis and data science, covering Python, SQL, ML, visualization, roadmaps, and interview prep.
A lightweight and intuitive Go library for data manipulation, statistics, and machine learning using DataFrames.
A general-purpose machine learning library for Rust, focusing on speed and ease of use with minimal dependencies.
A comprehensive Julia package for probability distributions, providing properties, PDFs, sampling, and maximum likelihood estimation.
A high-performance, fully-featured CSV parser and serializer for modern C++ with streaming, random access, and robust format handling.
An open-source Java framework for rapid development of machine learning and statistical applications with large dataset support.
A Ruby library for data analysis with DataFrame and Vector structures, offering storage, manipulation, and visualization.
A .NET library for data and time series manipulation with structured data frames, designed for scientific programming.
A Python package providing specialized statistical algorithms for graph and network analysis.
A meta gem that bundles scientific computing and visualization libraries for Ruby, enabling data analysis and plotting.
A MATLAB toolbox for advanced analysis of MEG, EEG, and iEEG data, developed at the Donders Institute.
A JavaScript library for linear least-squares curve fitting and regression analysis.
A command-line ASCII kanban board for managing personal and team todos using CSV files, with scripting and statistics.
API observability middleware for Node.js microservices, tracing calls and monitoring performance, health, and usage statistics.
Learn statistics through Python with real-world examples like analyzing marijuana price data across US states.
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