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    "info": {
        "author": "James D. Triveri",
        "author_email": "james.triveri@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "## `trikit` - Actuarial Reserving Methods in Python\n\n`trikit` is a collection of Loss Reserving utilities developed to facilitate\nActuarial analysis in Python, with particular emphasis on automating the basic\ntechniques generally used for estimating unpaid claim liabilities. `trikit`\ncurrently implements the Chain Ladder method for ultimate loss projection,\nalong with routines to compute the Chain Ladder prediction error, which can be\nused to quantify the variability around the ultimate loss projection\npoint estimates.\n\nIn addition to the library's core Chain Ladder functionality, `trikit`\nexposes a convenient interface that links to the Casualty Actuarial Society's\nSchedule P Loss Rerserving Database. The database contains information on\nclaims for major personal and commercial lines for all property-casualty\ninsurers that write business in the U.S[1]. For more information on\n`trikit`'s Schedule P Loss Reserving Database API, check out the official\ndocumentation [here](https://github.com/jtrive84/trikit/docs).\n\n\n## Installation\n\n`trikit` can be installed by running:\n\n```sh\n$ pip install trikit\n```\n\nAlternatively, manual installation can be accomplished by downloading the\nsource archive, extracting the contents and running:\n\n```sh\n$ python setup.py install\n```\n\n\n## Relevant Links\n- [trikit Quickstart Guide](https://github.com/jtrive84/trikit/docs/quickstart)\n- [trikit Documentation](https://github.com/jtrive84/trikit/docs)\n- [trikit Source](https://github.com/jtrive84/trikit)\n- [CAS Loss Reserving Database](https://www.casact.org/research/index.cfm?fa=loss_reserves_data)\n- [Python](https://www.python.org/)\n- [Numpy](http://www.numpy.org/)\n- [Pandas](https://pandas.pydata.org/)\n- [Matplotlib](https://matplotlib.org/)\n- [Seaborn](https://seaborn.pydata.org/)\n\n\n\n\n\n### Footnotes     \n\n[1] https://www.casact.org/research/index.cfm?fa=loss_reserves_data\n\n\n",
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        "name": "trikit",
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        "summary": "Actuarial Reserving Methods in Python",
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