A comprehensive, dependency-free statistics library for Go with extensive mathematical functions and thorough testing.
Stats is a comprehensive statistics library for the Go programming language that provides mathematical functions for data analysis, probability, and statistical computations. It solves the problem of needing reliable statistical tools in Go applications without introducing external dependencies, offering everything from basic descriptive statistics to advanced regression and distribution functions.
Go developers who need statistical analysis capabilities in their applications, particularly those working in data science, research, analytics, or any domain requiring mathematical computations.
Developers choose Stats because it's a mature, well-tested library with zero dependencies, comprehensive functionality, and excellent documentation—providing a complete statistical toolkit without the bloat of larger mathematical packages.
A well tested and comprehensive Golang statistics library package with no dependencies.
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Includes a wide range of statistical operations from basic descriptive statistics to regression and probability distributions, as evidenced by the extensive API listing in the README.
Pure Go implementation with zero external packages ensures lightweight integration, easy maintenance, and avoids dependency conflicts, aligning with Go's philosophy.
Returns clear error types for invalid inputs like empty data or NaN values, enhancing reliability in production environments.
Maintains 100% test coverage with thorough testing, as highlighted in the README, ensuring correctness and stability for critical applications.
Focuses on core statistical functions; lacks support for niche or cutting-edge techniques like multivariate analysis, which might necessitate other libraries.
Designed for batch data processing without incremental statistics functions, making it less suitable for real-time or streaming data scenarios.
Pure Go implementation means it doesn't leverage optimized C or Fortran libraries, which could impact performance for extremely large datasets compared to hybrid solutions.