CGMissingDataR: Impute Missing Glucose Values in CGM Data

Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) ), fits Random Forest regression models (Breiman (2001) ) and k-nearest-neighbor regression models (Zhang (2016) ), and reports mean absolute percentage error and R-squared across missingness rates.