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        "description": "FEERCI: A Package for Fast non-parametric confidence intervals for Equal Error Rates\n******************************************\n\n\n**FEERCI** is an opinionated, easy-to-use package for calculating EERs and non-parametric confidence intervals efficiently. It offers a single method, ``feerci.feerci``, that returns both an EER and CI for provided impostor and genuine scores. The only dependency is numpy.\n\nInstallation\n=======\n``pip install feerci``\n\nWhat's New\n=======\n0.2.0\n--------\n- Switched output arguments around, to make more intuitive sense\n0.1.0\n--------\n- Initial release of package\n\n\nLicense\n=====\n**FEERCI** is distributed under the MIT license\n\nVersion\n=====\n0.2.0\n\nExamples\n======\nCalculating just an EER::\n\n    import feerci\n    import numpy as np\n    impostors = np.random.rand(100)\n    genuines = np.random.rand(100)\n    eer,_,_,_ = feerci.feerci(impostors,genuines,is_sorted=False,m=-1)\n\nCalculating an EER and 95% confidence interval::\n\n    eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False)\n\nCalculating an EER and 99% confidence interval::\n\n    eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False,ci=.99)\n\nCalculating an EER and 99% confidence interval on 1000 bootstrap iterations::\n\n    eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False,m=1000,ci=.99)\n\n\n\n",
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