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  "Title": "Impute Missing Glucose Values in CGM Data",
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  "Description": "Imputes missing glucose values in repeated-measures\ncontinuous glucose monitoring (CGM) data. Workflows create\ntime-series features from raw timestamps, support model\nselection, and return the user's original columns plus an\nimputed glucose column. Methods include multiple imputation by\nchained equations (MICE; Azur et al. (2011)\n<doi:10.1002/mpr.329>), Random Forest regression (Breiman\n(2001) <doi:10.1023/A:1010933404324>), k-nearest-neighbor\nregression (Zhang (2016) <doi:10.21037/atm.2016.03.37>),\nXGBoost (Chen and Guestrin (2016)\n<doi:10.1145/2939672.2939785>), LightGBM (Ke et al. (2017)\n<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>),\nand ARIMA forecasting with the forecast framework (Hyndman and\nKhandakar (2008) <doi:10.18637/jss.v027.i03>). A\nPython-compatible backend uses 'reticulate' to call 'pandas',\n'scikit-learn', 'statsmodels', Python 'xgboost', and optional\nPython 'lightgbm'.",
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