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