Package: CGMissingDataR
Title: Missingness Benchmark for Continuous Glucose Monitoring Data
Version: 0.0.1
Authors@R: c(
    person("Shubh", "Saraswat", email = "shubh.saraswat00@gmail.com", role = c("cre", "aut", "cph"),
    comment = c(ORCID = "0009-0009-2359-1484")),
    person("Hasin Shahed Shad", email = "hasin.shad@uky.edu", role = "aut"),
    person("Xiaohua Douglas", "Zhang", email = "douglas.zhang@uky.edu", role = "aut",
    comment = c(ORCID = "0000-0002-2486-7931")))
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.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 4.3)
RoxygenNote: 7.3.3
Imports: mice, FNN, Metrics, ranger
Suggests: testthat (>= 3.0.0), spelling, knitr, rmarkdown
Config/testthat/edition: 3
NeedsCompilation: no
Language: en-US
URL: https://github.com/saraswatsh/CGMissingDataR,
        https://saraswatsh.github.io/CGMissingDataR/
BugReports: https://github.com/saraswatsh/CGMissingDataR/issues
LazyData: true
VignetteBuilder: knitr
Packaged: 2026-01-29 02:57:52 UTC; shubh
Author: Shubh Saraswat [cre, aut, cph] (ORCID:
    <https://orcid.org/0009-0009-2359-1484>),
  Hasin Shahed Shad [aut],
  Xiaohua Douglas Zhang [aut] (ORCID:
    <https://orcid.org/0000-0002-2486-7931>)
Maintainer: Shubh Saraswat <shubh.saraswat00@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-03 10:30:15 UTC
Built: R 4.6.0; ; 2026-04-25 13:37:41 UTC; unix
