Packaged: 2019-02-11 22:51:16 UTC; timvigers

Packaged: 2019-03-26 14:47:23 UTC; timvigers

Packaged: 2019-05-22 19:10:15 UTC; timvigers

Imports: ggplot2, lubridate, pracma, zoo, pastecs, tools, readxl, readr
Packaged: 2019-10-24 15:44:59 UTC; timvigers

Imports: ggplot2, lubridate, pracma, zoo, pastecs, tools, readxl,
Packaged: 2020-03-12 20:28:00 UTC; timvigers

Packaged: 2020-02-18 20:54:52 UTC; timvigers

Description: This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.
Packaged: 2021-02-15 17:45:04 UTC; timvigers

Authors@R: person("Tim", "Vigers", email = "timvigers@mailbox.org", role = c("aut", "cre"))
Packaged: 2021-06-08 16:59:12 UTC; timvigers

Imports: ggplot2, parsedate, lubridate, pracma, zoo, pastecs, tools,
Packaged: 2022-07-06 22:54:56 UTC; timvigers

Authors@R: person("Tim", "Vigers", email = "timothy.vigers@cuanschutz.edu", role = c("aut", "cre"))
Packaged: 2022-09-07 19:01:26 UTC; timvigers

Packaged: 2022-09-30 20:52:03 UTC; timvigers

Packaged: 2022-10-24 16:21:50 UTC; timvigers

Packaged: 2020-04-29 22:05:07 UTC; timvigers

Title: Analyzing Glucose and Glucose Variability
Authors@R: c(person("Maria", "Henriquez", role = c("aut", "com", "cph", "cre", "trl"), email = "marhenriq@gmail.com"),
person("Brinnae", "Bent", role = c("aut", "cph", "dtc"),
Imports: dplyr, tidyverse, ggplot2, hms, stats, magrittr
Description: 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>
Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
Author: Maria Henriquez [aut, com, cph, cre, trl],