Statistics
Showing 36 of 221 projects
An open-source numerical library for .NET and Mono providing algorithms for scientific computing, linear algebra, statistics, and more.
A lightweight, dependency-free JavaScript library for descriptive, regression, and inference statistics.
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
A Laravel package for retrieving Google Analytics data, including pageviews, visitors, and top pages, with a fluent API.
A high-performance, easy-to-use, and scalable machine learning package for linear models, factorization machines, and field-aware factorization machines.
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.
A curated collection of R tutorials, packages, and resources for Data Science, NLP, and Machine Learning.
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
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 curated collection of 500+ resources for data analysis and data science, covering Python, SQL, ML, visualization, roadmaps, and interview prep.
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 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 Python package providing specialized statistical algorithms for graph and network analysis.
A .NET library for data and time series manipulation with structured data frames, designed for scientific programming.
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.
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