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Changelog

Source: NEWS.md

CGMissingDataR 0.0.2

Major changes

  • run_missing_glucose_imputation() now handles both explicit missing glucose values and missing readings implied by timestamp gaps. When timestamps skip expected CGM intervals, the function regularizes each subject to the expected interval and imputes the newly created missing glucose rows.

  • The returned data frame is now simpler. It contains the user’s original columns plus imputed_glucose_value. Internal columns used for timestamp regularization, lag features, rolling means, model fitting, and missingness tracking are no longer returned.

  • The original glucose column is still preserved. Values that were originally missing, or created from timestamp gaps, remain NA in the original target column, while completed values are stored in imputed_glucose_value.

  • imputed_glucose_value is returned as a continuous numeric model estimate. Users who need whole-number glucose values for reporting can round this column after imputation.

  • run_missing_glucose_imputation() now supports selectable real-imputation methods through the existing models argument. The default models = "auto" keeps the missing-rate rule, using MICE+ARIMA when missingness is at or below the configured threshold and MICE+XGBoost otherwise.

  • Users can now force one final real-imputation method with models = "arima", "xgboost", "rf", "knn", or "lightgbm". Random Forest, kNN, and LightGBM use the same lag-feature workflow as the existing ARIMA and XGBoost real-imputation paths.

  • Real-imputation model engines now use n_threads = 1 by default for CRAN-friendly and shared-system-friendly CPU use. Users can increase n_threads for faster local XGBoost, Random Forest, or LightGBM runs.

  • Added a bundled Shiny app for interactive missing glucose imputation. The app lets users upload a CSV file or load example data, choose the relevant columns, select the final imputation method, run imputation, preview results, and download the completed data.

  • Added built-in example data for demonstrating both explicit missing glucose values and timestamp-gap handling.

  • The optional Python-compatible backend remains available with imputer_backend = "sklearn". The default backend remains imputer_backend = "mice" for standard R usage. Both backends support the selectable final imputation methods, with Python LightGBM available when the optional Python lightgbm module is installed.

  • Updated README and vignettes to describe timestamp-gap handling, the simplified output structure, selectable final imputation methods, the bundled Shiny app, backend options, and post-imputation rounding.

CGMissingDataR 0.0.1

CRAN release: 2026-02-03

  • Initial package creation preparing for CRAN submission.