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 |
| 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:
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