A comprehensive Ruby suite for performing basic and advanced statistical analysis, including regression, factor analysis, and reliability testing.
Statsample is a comprehensive Ruby library for statistical analysis, offering tools for descriptive statistics, correlation, regression, factor analysis, reliability testing, and hypothesis testing. It solves the problem of performing advanced statistical computations within the Ruby programming environment, integrating with R for specialized methods like structural equation modeling.
Ruby developers, data scientists, and researchers who need to perform statistical analysis within Ruby applications or interactive sessions, especially those already familiar with the SciRuby ecosystem.
Developers choose Statsample for its extensive statistical capabilities, clean modular API, and seamless integration with Ruby data structures via daru, providing a native Ruby alternative to statistical tools like R or Python's SciPy.
A suite for basic and advanced statistics on Ruby.
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Covers everything from descriptive statistics to advanced techniques like structural equation modeling and dominance analysis, as outlined in the Key Features and README.
Each analysis type is encapsulated in its own class with a clear API, making it intuitive for interactive sessions and extensible, per the Philosophy section.
Integrates with R's sem and OpenMx libraries via the statsample-sem gem, allowing access to robust structural equation modeling tools within Ruby.
Provides numerous IRuby notebooks for statistics and visualizations, offering practical guidance on usage, as listed in the README's Usage section.
Requires installation of GSL, R, and specific R libraries like irr and Rserve, which adds setup overhead and potential cross-platform issues, especially on non-Linux systems.
Being a Ruby library, it has a smaller statistical community and fewer third-party extensions compared to Python or R, limiting advanced or niche analyses.
Ruby's interpreted nature and dependency on C extensions for optimization may lead to slower computations for intensive statistical operations on large datasets.