Analyzing Glucose and Glucose Variability R package cgmquantify version 0.1.0 Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract> American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc> Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>. Software Depends: R (>= 2.10) Imports: dplyr, tidyverse, ggplot2, hms, stats, magrittr Suggests: testthat (>= 2.0.0), knitr, rmarkdown Maria Henriquez <marhenriq@gmail.com> Comprehensive R Archive Network (CRAN) Maria Henriquez [aut, com, cph, cre, trl], Brinnae Bent [aut, cph, dtc] MIT License + file LICENSE (https://CRAN.R-project.org/package=cgmquantify/LICENSE) 2021-02-05 application/tgz https://CRAN.R-project.org/package=cgmquantify doi:10.32614/CRAN.package.cgmquantify