Core functions at a glance | Loading Sample Data | 1. Basic GRID Analysis | 2. Hyperglycemic Event Detection | 3. Hypoglycemic Event Detection | 4. Comprehensive Event Detection | 5. Local Maxima Detection | 6. Maxima-GRID Combined Analysis | 7. Excursion Analysis | 8. Advanced Pipeline: Complete Workflow | 9. Time-Based Analysis Functions | 10. Data Ordering Utility | 11. Visualization Examples | 12. Performance Comparison | Summary | Core Analysis Functions: | Advanced Pipeline Functions: | Utility Functions: | Key Features: | References

Overview | Notes | GRID | Detect all events | Detect hyperglycemic events | Detect hypoglycemic events | Excursion | Local maxima | Max after hours | Max before hours | Min after hours | Min before hours | Maxima + GRID combined | Ordering utility | Start finder | Transform dataframe | Function reference and help

Relationship to iglu | Datasets | iglu: episode_calculation | cgmguru: detect_all_events | Speed comparison | References

detect_between_maxima | Overview | Inputs | Returns | Run documented examples

detect_hyperglycemic_events

detect_hypoglycemic_events

excursion | Overview | Inputs | Returns | Run documented examples

find_local_maxima

find_max_after_hours

find_max_before_hours

find_min_after_hours

find_min_before_hours

find_new_maxima

Introduction | Example Data | Basic GRID Analysis | Parameter Sensitivity | Larger Dataset Example | Plotting GRID Events Over Time | Conclusion | How to Open this Vignette in RStudio

maxima_grid

mod_grid

orderfast | Overview | Inputs | Returns | Run documented examples

start_finder | Overview | Inputs | Returns | Run documented examples

transform_df | Overview | Inputs | Returns | Run documented examples