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        "author": "Rowan Swiers",
        "author_email": "rowan.swiers@astrazeneca.com",
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        "description": "## CensorFix\n\n[![Documentation Status](https://readthedocs.org/projects/censor-fix/badge/?version=latest)](https://censor-fix.readthedocs.io/en/latest/?badge=latest)\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n\nPlease look at the website https://censor-fix.readthedocs.io/en/latest/\n\n\nA library for multiple imputation of censored data.\n\nThis software performs multiple imputation on censored data using the probabilistic programming langauge (ppl) stan. \nFor 1d imputation the library can fits a distribution to data to create imputations. \n\nFor missing data with many features the library can model the censored values using the other features in a round robin fashion\n\nHow to use:\n\nThe data needs to be in a pandas dataframe and the columns that need imputing must be specified.\nThe data is assumed to have a normal distribution unless stated otherwise but there are options for the data to be uniform,t distributed or exponential. To make the data more normal you can try using sklearn.reprocessing.power_transform to make the data normal. \n\n\n\n\n\n",
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