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            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Science/Research",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "[![Build Status](https://travis-ci.org/aschleg/mathpy.svg?branch=master)](https://travis-ci.org/aschleg/mathpy)\n[![Build status](https://ci.appveyor.com/api/projects/status/8gtsv0afqt29qg56/branch/master?svg=true)](https://ci.appveyor.com/project/aschleg/mathpy/branch/master)\n[![Coverage Status](https://coveralls.io/repos/github/aschleg/mathpy/badge.svg?branch=master)](https://coveralls.io/github/aschleg/mathpy?branch=master)\n\n## About mathpy\n\nmathpy is a collection of mathematical and statistical functions encompassing several different\nfields such as linear algebra, numerical analysis, and number theory. The package is intended to\nwork with other popular scientific computing packages such as numpy and scipy. Most functions in\nThe mathpy package are also available as Excel UDFs for users who prefer working in Excel. The\nmathpy project is primarily a personal project intended to develop a deeper understanding of\nthe mathematics and algorithmic implementations of various mathematical topics.\n\n## Documentation\n\nThe documentation of the mathpy package is available here:\n\nhttps://aschleg.github.io/mathpy/\n\nThe documentation for importing the mathpy functions to use in Excel as UDFs is found here:\n\nhttps://aschleg.github.io/mathpy/excel.html\n\n## Installation\n\nMathpy is easily installed through `pip` and is the most recommended approach.\n\n~~~~\npip install mathpy\n~~~~\n\nThe package should also be able to install from Github with the following command:\n\n~~~~\npip install git+https://github.com/aschleg/mathpy.git\n~~~~\n\nThe Github repository can also be cloned and installed with the included `setup.py` script.\n\n~~~~\ngit clone https://github.com/aschleg/mathpy.git\npython setup.py install\n~~~~\n\n## Installation Requirements\n\n* Python 2.7 or 3+\n    - Recommended to install the Anaconda distribution for your preferred version of Python if not installed already.\n* Compatible with Windows, Mac and Linux OS.\n    - Excel UDFs are currently available in Windows only.\n\n## Available Methods\n\n* Linear Algebra\n    - Matrix Decomposition\n    - Vector and Matrix Norms\n    - Matrix Tests\n\n* Numerical Analysis\n    - Numerical Differentiation\n    - Numerical Integration\n    - Polynomial Evaluation\n    - Polynomial Interpolation\n    - Roots of Polynomials\n\n* Number Theory\n    - Binomial Coefficient\n    - Factorials\n    - Integer Factorization\n    - Greatest Common Divisor\n    - Prime Numbers\n    - Number Sequences\n\n* Probability Distributions\n    - Continous Distributions\n        + Uniform\n    - Discrete Distributions\n        + Bernoulli\n        + Binomial\n\n* Random Sampling and PRNGs (Pseudorandom Number Generators)\n    - Continuous Distributions\n        + Uniform\n    - Discrete Distributions\n        + Bernoulli\n        + Binomial\n    - Pseudorandom Number Generators\n        + Linear Congruential Generator\n        + Combined Linear Congruential Generators\n        + Lehmer Random Number Generator (Multiplicative Congruential Generator)\n\n* Set Theory\n    - Extensions to Python set class\n    - Multiple union and intersection operations\n    - Cartesian products\n    - Relative Complements of multiple sets\n\n* Statistics\n    - Factor Analysis\n    - ANOVA and MANOVA\n    - Hypothesis Testing\n    - Covariance and Correlation Matrices\n    - Variance\n    - Simulating Correlation Matrices\n\n### License\n\nMIT\n",
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