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) tools:::Rd_expr_doi("10.1002/mpr.329")), fits Random Forest regression models (Breiman (2001) tools:::Rd_expr_doi("10.1023/A:1010933404324")) and k-nearest-neighbor regression models (Zhang (2016) tools:::Rd_expr_doi("10.21037/atm.2016.03.37")), and reports mean absolute percentage error and R-squared across missingness rates.
Maintainer: Shubh Saraswat shubh.saraswat00@gmail.com (ORCID) [copyright holder]
Authors:
Hasin Shahed Shad hasin.shad@uky.edu
Xiaohua Douglas Zhang douglas.zhang@uky.edu (ORCID)