Statistical functions and distributions for Elixir, including descriptive statistics and probability distributions.
Statistics is an Elixir library that provides statistical functions and probability distributions for data analysis. It enables developers to perform calculations like median, variance, and random sampling from distributions directly within their Elixir applications. The library solves the need for native statistical computation tools in the Elixir ecosystem without requiring external dependencies.
Elixir developers working on data analysis, scientific computing, or applications requiring statistical calculations. Data scientists and researchers using Elixir for prototyping or production statistical models.
Developers choose Statistics because it offers pure Elixir implementations of statistical concepts, making it easy to integrate into Elixir projects without external dependencies. Its transparency and educational approach allow users to understand the underlying algorithms while providing practical statistical tools.
Statistical functions and distributions for Elixir
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All functions are implemented directly in Elixir without external dependencies, enabling seamless integration into Elixir projects. Evidence from README: 'Everything is implemented in Elixir.'
Focuses on correctness and learning, allowing developers to understand statistical algorithms through clear implementations. Philosophy prioritizes educational value over raw performance.
Available on Hex.pm with Mix support, making installation straightforward by adding it as a dependency in mix.exs. Usage section shows simple setup steps.
Provides descriptive statistics like median and variance, plus probability distributions such as Gaussian, as outlined in the key features list.
Explicitly not designed for speed, using slow approximations and trial-and-error methods that hinder efficiency. README states: 'not a library to use if you need fast computation.'
Relies on approximations and numerical integrations, which may not meet high-accuracy needs. Evidence: README mentions 'slow approximations, numerical function integration, or trial-and-error methods.'
Version 0.6 indicates it's still evolving, with potential for missing features, bugs, or instability, limiting its reliability for production use.