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 using 'mice' (Azur et al. (2011) doi:10.1002/mpr.329), Random Forest regression using 'ranger' (Breiman (2001) doi:10.1023/A:1010933404324), k-nearest-neighbor regression using 'FNN' (Zhang (2016) doi:10.21037/atm.2016.03.37), 'XGBoost' using 'xgboost' (Chen and Guestrin (2016) doi:10.1145/2939672.2939785), 'LightGBM' using 'lightgbm' (Ke et al. (2017) https://papers.nips.cc/paper/6907-lightgbm-a- highly-efficient-gradient-boosting-decision), and ARIMA forecasting using 'forecast' (Hyndman and Khandakar (2008) doi:10.18637/jss.v027.i03). A 'Python'-compatible backend uses 'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', 'xgboost', and optional 'lightgbm'.