Renamed the package from CGMissingDataR to imputeCGM.
Updated package metadata, GitHub links, pkgdown links, README content,
vignette names, manual pages, tests, and Shiny app package references for the
new imputeCGM package identity.
Documentation now points to the new GitHub repository named imputeCGMr.
Addressed CRAN resubmission feedback by quoting software and package names in
DESCRIPTION, replacing the Shiny app example wrapper with
if (interactive()), adding Shiny helper test coverage, requiring explicit
export paths for CSV writing, and making seed setting opt-in with
seed = NULL by default.
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.