A Swift library providing a comprehensive collection of functions for statistical calculations.
SigmaSwiftStatistics is a Swift library that provides a comprehensive collection of functions for statistical calculations. It solves the problem of performing reliable statistical analysis within Swift applications, offering functions for measures like mean, standard deviation, variance, correlation, and distributions. The library ensures compatibility with results from tools like Excel and R.
Swift developers building iOS, macOS, or cross-platform applications that require statistical computations, such as data analysis tools, research apps, or financial calculators.
Developers choose SigmaSwiftStatistics for its accuracy, extensive function set matching industry standards, and seamless integration into Swift projects via multiple dependency managers. Its focus on edge-case handling and compatibility with established statistical software makes it a reliable choice.
A collection of functions for statistical calculation written in Swift.
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Functions are designed to match results from Microsoft Excel and R, ensuring consistency for users familiar with these tools, as shown in the README for functions like average and standard deviation.
Includes a wide range of statistical functions from mean to nine quantile methods, covering most common descriptive statistics needs in Swift apps, as listed in the README with over 20 functions.
Returns nil for empty arrays or invalid inputs, preventing crashes and ensuring safe usage in production code, exemplified in functions like average and standard deviation.
Implements nine quantile calculation methods from the Hyndman and Fan paper, providing flexibility for different statistical requirements, detailed in the quantiles section.
Lacks support for inferential statistics like t-tests, ANOVA, or regression analysis, limiting its use for more complex data science workflows beyond descriptive statistics.
Requires managing different library versions for legacy Swift, as noted in the README's legacy section, which can complicate project setup and maintenance.
Primarily works with arrays and lacks built-in support for streaming data or integration with Swift data frameworks like SwiftData or Core Data, making it less suitable for dynamic datasets.