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        "author": "Stephen J. Mildenhall",
        "author_email": "mildenhs@stjohns.edu",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Education",
            "Intended Audience :: Financial and Insurance Industry",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3",
            "Topic :: Education",
            "Topic :: Office/Business :: Financial",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# aggregate\n\n\na powerful aggregate loss modeling library for Python\n=====================================================\n\nWhat is it?\n-----------\n\n**aggregate** is a Python package providing fast, accurate, and expressive data\nstructures designed to make working with probability distributions\neasy and intuitive. Its primary aim is to be an educational tool, allowing\nexperimenation with complex, **real world** distributions. It has applications in\ninsurance, risk management, actuarial science and related areas.\n\nMain Features\n-------------\n\nHere are just a few of the things that ``aggregate`` does well:\n\n  - Output in tabular form using Pandas\n  - Human readable persistence in YAML\n  - Built in library of insurance severity curves for both catastrophe and non\n    catastrophe lines\n  - Built in parameterization for most major lines of insurance in the US, making it\n    easy to build a \"toy company\" based on market share by line\n  - Clear distinction between catastrophe and non-catastrohpe lines\n  - Use of Fast Fourier Transforms throughout differentiates ``aggregate`` from\n    tools based on simulation\n  - Fast, accurate - no simulations!\n  - Graphics and summaries following Pandas and Matplotlib syntax\n\n\nPotential Applications\n----------------------\n\n  - Education\n       * Building intuition around how loss distribtions convolve\n       * Convergence to the central limit theorem\n       * Generalized distributions\n       * Compound Poisson distributions\n       * Mixed distributiuons\n       * Tail behavior based on frequency or severity tail\n       * Log concavity properties\n  - Pricing small insurance portfolios on a claim by claim basis\n  - Analysis of default probabilities\n  - Allocation of capital and risk charges\n  - Detailed creation of marginal loss distributions that can then be\n    sampled and used by other simulation software, e.g. to incorporate\n    dependence structures, or in situations where it is necessary to\n    track individual events, e.g. to compute gross, ceded and net bi-\n    and trivariate distributions.\n\nMissing Features\n----------------\n\nHere are some important things that ``aggregate`` does **not** do:\n\n  - It is strictly univariate. It is impossible to model bivariate or multivariate distributions.\n    As a result ``aggregate`` is fast and accurate\n  - ``aggregate`` can model correlation between variables using shared mixing variables. This\n    is adequate to build realistic distributions but would not be adequate for an industrial-\n    strength insurance company model.\n\nDocumentation\n-------------\n\nhttp://www.mynl.com/aggregate/index.html\n\n\nWhere to get it\n---------------\n\n* The source code is currently hosted on GitHub at:\n* https://github.com/mynl/aggregate\n\n\nInstallation\n------------\n\npip install aggregate\n\n\nDependencies\n------------\n\n- [NumPy](https://www.numpy.org): 1.9.0 or higher\n- [Pandas](https://github.com/pandas-dev/pandas): 0.23.0 or higher\n\nLicense\n-------\n\n[BSD 3](LICENSE)\n\nContributing to aggregate\n-------------------------\n\nAll contributions, bug reports, bug fixes, documentation improvements,\nenhancements and ideas are welcome.\n\n",
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