Statistics
Showing 18 of 54 projects
A comprehensive, dependency-free statistics library for Go with extensive mathematical functions and thorough testing.
A Rack-based A/B testing framework for Ruby web applications, designed to work with Rails, Sinatra, or any Rack-based app.
A comprehensive collection of machine learning algorithms and mathematical utilities implemented in JavaScript for browser and Node.js.
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 music scrobble database for creating personal listening statistics and charts.
A self-hosted, open-source dashboard for visualizing and analyzing personal Strava activity data.
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 collection of programming articles covering C++, Elm, Haskell, Kotlin, statistics, and software development concepts.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.