A comprehensive, self-contained mathematics library for PHP with no external dependencies, covering algebra, statistics, linear algebra, and numerical analysis.
MathPHP is a comprehensive mathematics library for PHP that provides hundreds of mathematical functions across multiple domains. It solves the problem of performing advanced mathematical computations in PHP without requiring external C extensions or services, offering everything from basic arithmetic to specialized functions like matrix decompositions and statistical distributions.
PHP developers building applications that require mathematical computations, such as data analysis tools, scientific calculators, educational software, financial applications, or machine learning prototypes.
Developers choose MathPHP because it's a self-contained, dependency-free library written entirely in PHP, making it easy to install and integrate. It offers an exceptionally broad range of mathematical capabilities unmatched by other PHP libraries, with clear object-oriented APIs and extensive documentation.
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra
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The library spans algebra, statistics, linear algebra, numerical analysis, finance, and more, with over 20 probability distributions and matrix decompositions like SVD and Cholesky, making it a comprehensive toolkit.
As a pure PHP library, it requires no external extensions or systems, simplifying installation via Composer and ensuring seamless integration into any PHP project.
Features intuitive classes like MatrixFactory and Polynomial, with method-based operations (e.g., $matrix->inverse()) and extensive usage examples in the README for easy adoption.
Includes classic datasets such as mtcars and iris, allowing developers to experiment with statistical functions without sourcing external data.
Being implemented purely in PHP without native extensions, it can be slower for computationally intensive tasks like large matrix operations compared to libraries using C bindings.
Handling big matrices or datasets may lead to high memory consumption, as the library lacks optimizations for sparse data structures or streaming processing.
While broad, it misses some niche areas like advanced machine learning, signal processing, or GPU acceleration, which are covered by specialized libraries in other languages.