Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)<doi:10.1093/bioinformatics/btx826>'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.

Tools for advanced analysis of continuous glucose monitoring (CGM)
    time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based
    algorithms for postprandial peak detection, and detection of hypoglycemic and
    hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus
    CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to
    provide accurate and fast analysis on large datasets.

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'.