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        "author": "Abhinav Tushar",
        "author_email": "lepisma@fastmail.com",
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        "classifiers": [
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        "description": "# ledge\n\n[![Build\nStatus](https://img.shields.io/travis/reichlab/ledge.svg?style=flat-square)](https://travis-ci.org/reichlab/ledge) [![Pypi](https://img.shields.io/pypi/v/ledge.svg?style=flat-square)](https://pypi.python.org/pypi/ledge)\n\nHedging algorithms with lag.\n\nBesides the regular setting of 'prediction with expert advice', we are\ninterested in working with truth values with _lags_. This results in a partially\nobserved truth value for the present (and the past) at each step. In a discrete\ntime setting with delays in accurate characterization of _final_ truth, a truth\nvalue is specified by:\n\n1. `timepoint`: The time itself\n2. `lag`: Time the value was revealed - `timepoint`\n\n## Usage\n\n`ledge` is a composed from a bunch of types and functions. It works with\n[DataArrays](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.html#xarray.DataArray)\nfor model _predictions_, _losses_ and _truths_.\n\nThere is no single model and the user is supposed to _compose_ a model using the\ncomponents in here. Each module is written as a literate\n[org-mode](https://orgmode.org/) file and contains usage documentation for the\ncontaining functions. Start with the main file `./ledge/README.org`.\n\n\n",
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