CGMissingDataR: Missingness Benchmark for Continuous Glucose Monitoring 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) <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.

Version:	0.0.1
Depends:	R (≥ 4.3)
Imports:	mice, FNN, Metrics, ranger
Suggests:	testthat (≥ 3.0.0), spelling, knitr, rmarkdown
Published:	2026-02-03
DOI:	10.32614/CRAN.package.CGMissingDataR (may not be active yet)
Author:	Shubh Saraswat [cre, aut, cph], Hasin Shahed Shad [aut], Xiaohua Douglas Zhang [aut]
Maintainer:	Shubh Saraswat <shubh.saraswat00 at gmail.com>
BugReports:	https://github.com/saraswatsh/CGMissingDataR/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/saraswatsh/CGMissingDataR, https://saraswatsh.github.io/CGMissingDataR/
NeedsCompilation:	no
Language:	en-US
Materials:	README, NEWS
CRAN checks:	CGMissingDataR results

Documentation:

Reference manual:	CGMissingDataR.html , CGMissingDataR.pdf
Vignettes:	How To Use CGMissingDataR (source, R code)

Downloads:

Package source:	CGMissingDataR_0.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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