{
    "info": {
        "author": "Stefan Appelhoff",
        "author_email": "stefan.appelhoff@mailbox.org",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "[![Build Status](https://travis-ci.org/sappelhoff/remedian.svg?branch=master)](https://travis-ci.org/sappelhoff/remedian)\n[![codecov](https://codecov.io/gh/sappelhoff/remedian/branch/master/graph/badge.svg)](https://codecov.io/gh/sappelhoff/remedian)\n[![Documentation Status](https://readthedocs.org/projects/remedian/badge/?version=latest)](http://remedian.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/remedian.svg)](https://badge.fury.io/py/remedian)\n\n# remedian\nThe  Remedian:  A  Robust  Averaging  Method  for  Large  Data  Sets - Python\nimplementation\n\nThis algorithm is used to approximate the median of several data chunks if\nthese data chunks cannot (or should not) be loaded into memory at once.\n\nGiven a data chunk of size `obs_size`, and `t` data chunks overall, the\nRemedian class sets up a number `k_arrs` of arrays of length `n_obs`.\n\nThe median of the `t` data chunks of size `obs_size` is then approximated\nas follows: One data chunk after another is fed into the `n_obs` positions\nof the first array. When the first array is full, its median is calculated\nand stored in the first position of the second array. After this, the first\narray is re-used to fill the second position of the second array, etc.\nWhen the second array is full, the median of its values is stored in the\nfirst position of the third array, and so on.\n\nThe final \"Remedian\" is the median of the last array, after all `t` data\nchunks have been fed into the object.\n\n# Installation\n\n`pip install remedian`\n\nThe dependencies should be installed automatically by pip.\n\n# Installation of most recent version\n\n1. activate your python environment\n2. `git clone https://www.github.com/sappelhoff/remedian`\n3. `cd remedian`\n5. `pip install -e .`\n6. then you should be able to `from remedian.remedian import Remedian`\n\n# Usage\n\nSee the [example in the docs](https://remedian.readthedocs.io/en/latest/examples.html).\n\n# References\n\n> P.J. Rousseeuw, G.W. Bassett Jr., \"The remedian: A robust averaging method\n  for large data sets\", Journal of the American Statistical Association, vol.\n  85 (1990), pp. 97-104\n\n> M. Chao, G. Lin, \"The asymptotic distributions of the remedians\", Journal of\n  Statistical Planning and Inference, vol. 37 (1993), pp. 1-11\n\n> Domenico Cantone, Micha Hofri, \"Further analysis of the remedian algorithm\",\n  Theoretical Computer Science, vol. 495 (2013), pp. 1-16\n\n\n",
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