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CGMissingDataR  

Missingness Benchmark for Continuous Glucose Monitoring Data
View on CRAN: Click here


Download and install CGMissingDataR package within the R console
Install from CRAN:
install.packages("CGMissingDataR")

Install from Github:
library("remotes")
install_github("cran/CGMissingDataR")

Install by package version:
library("remotes")
install_version("CGMissingDataR", "0.0.1")



Attach the package and use:
library("CGMissingDataR")
Maintained by
Shubh Saraswat
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-03
Latest Update: 2026-02-03
Description:
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) <doi:10.1002/mpr.329>), fits Random Forest regression models (Breiman (2001) <doi:10.1023/A:1010933404324>) and k-nearest-neighbor regression models (Zhang (2016) <doi:10.21037/atm.2016.03.37>), and reports mean absolute percentage error and R-squared across missingness rates.
How to cite:
Shubh Saraswat (2026). CGMissingDataR: Missingness Benchmark for Continuous Glucose Monitoring Data. R package version 0.0.1, https://cran.r-project.org/web/packages/CGMissingDataR. Accessed 04 Jul. 2026.
Previous versions and publish date:
0.0.1 (2026-02-03 11:30)
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Functions, R codes and Examples using the CGMissingDataR R package
Full CGMissingDataR package functions and examples
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