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        "author": "Danny Cunningham",
        "author_email": "djcunningham0@gmail.com",
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        "description": "rankaggretation\n===============\n\nThis package implements some rank aggregation methods in Python. Currently, the\nfollowing methods are supported:\n\n* Instant Runoff Voting\n* Borda count\n* Dowdall system (variant of Borda count)\n* \"Average rank\" -- just take the average rank of each item across all lists. I\n  don't know if there is a real name for this method.\n\nI plan on adding additional methods in the future (such as the Markov chain\nmethods described in `this paper \n<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.8702&rep=rep1&type=pdf>`_.\n\nAll methods currently implemented are designed to work with partial lists (i.e.,\neach base ranker does not necessarily rank every item).\n\nInstallation\n------------\n\nInstall from PyPI:\n\n``pip install rankaggregation``\n\n\nExample usage\n-------------\n\nThe key object in the package is the ``RankAggregator`` class. Each method takes\n``rank_list`` as a parameter. Each list in ``rank_list`` is the ranking preference\nof a single base ranker.\n\n>>> import rankaggregation as ra\n>>> agg = ra.RankAggregator()\n>>> rank_list = [['A', 'B', 'C'], ['B', 'A', 'C'], ['C', 'D', 'A']]\n>>> agg.instant_runoff(rank_list)\n['A', 'B', 'C', 'D']\n>>> agg.borda(rank_list)\n[('A', 9), ('B', 8.0), ('C', 8), ('D', 5.0)]\n>>> agg.dowdall(rank_list)\n[('A', 1.8333333333333333), ('C', 1.6666666666666665), ('B', 1.5), ('D', 0.5)]\n>>> agg.average_rank(rank_list)\n[('B', 1.5), ('A', 2.0), ('D', 2.0), ('C', 2.3333333333333335)]",
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