Ambulatory Glucose Profile (AGP) | Episode Calculation | Parameters | data | lv1_hypo, lv2_hypo, hy1_hyper, lv2_hyper | dur_length | Return value | Epicalc_profile function

Abstract | Introduction | Methods | Results | Discussion | Important Notes on rGV and cgmquantify | Conclusion | Appendix | Why are the first and last data points always treated as crossing points? | What is whiplash? | Why set a default of 180 minutes for max_gap? | References

Shiny App | Example Datasets | Example data for 1 subject | Example data for 5 subjects | Importing and Cleaning data | Importing data | Processing data | Calculating time-independent metrics | Example metrics: time-independent | Calculating time-dependent metrics | CGMS2DayByDay() | Example metrics: time-dependent | Calculating metrics for sleep or wake periods | Using calculate_sleep_wake | Visualizations | Example plots: time-series | Lasagna plots | Example plots: Rate of change

Introduction | Statement of need | Getting started | A simple example with two variables | Example with mtcars dataset | Example using latentcor with subsampling | References

Latent Gaussian Copula Model for Mixed Data | Moment-based estimation of $\Sigma$ based on bridge functions | Estimation methods | Original method (method = "original") | Approximation method (method = "approx") | Adjustment of pointwise-estimator for positive-definiteness | Appendix | Derivation of bridge function $F$ for ternary/truncated case | Derivation of approximate bound for the ternary/continuous case | Derivation of approximate bound for the ternary/truncated case | References