Metrics Reference

Each metric names the publication or reference implementation behind its contract when one applies. Follow the cited source for method details and limitations.

Time in ranges

Function	What it computes	Source
`calculateEnhancedTIR`	5-range TIR (very low / low / in-range / high / very high) with consensus target assessment	Battelino 2019
`calculatePregnancyTIR`	Pregnancy TIR (63–140 mg/dL)	ADA Standards of Care
`calculateTITR`	Time in Tight Range (70–140); target assessment uses the library's configurable 50% default	Project default
`detectEpisodes`	≥15-min hypo/hyper events with level, duration, nadir/peak	Danne 2017; Battelino 2019

Averages & estimation

Function	What it computes	Source
`estimateGMI`	Glucose Management Indicator from mean glucose	Bergenstal 2018
`estimateA1CFromAverage` / `estimateAvgGlucoseFromA1C`	A1C ⇄ eAG	Nathan 2008
`calculateHOMAIR`	Insulin resistance	Matthews 1985

Variability & risk

Function	What it computes	Source
`glucoseStandardDeviation` / `glucoseCoefficientOfVariation`	SD, CV%	ADA 2019
`glucoseMAGE`	Mean Amplitude of Glycemic Excursions	Service 1970
`glucoseLBGI` / `glucoseHBGI`	Low/High Blood Glucose Index	Kovatchev 2006
`calculateADRR`	Average Daily Risk Range	Kovatchev 2006
`calculateGRADE`	Glycemic Risk Assessment Diabetes Equation	Hill 2007
`calculateGRI`	Glycemia Risk Index with A–E zones	Klonoff 2023
`calculateJIndex`	Composite mean + variability	Wojcicki 1995
`calculateMODD`	Mean Of Daily Differences	Service 1980
`calculateCONGA`	Continuous Overall Net Glycemic Action	McDonnell 2005
`glucoseMValue`	Schlichtkrull M-value (with/without W correction)	Schlichtkrull 1965
`calculateIGC`	Rodbard hypo/hyper index + Index of Glycemic Control	Rodbard 2009 (+ erratum)
`calculateGVIPGS`	GVI + Patient Glycemic Status from unit-bearing `GlucoseReading[]` (Nightscout parity)	Rodbard; Nightscout
`glucoseMAG`	Mean Absolute Glucose change per hour	Hermanides 2010
`glucoseGVP`	Glycemic Variability Percentage	Peyser 2018
`calculateActivePercent`	Timestamp-slot coverage estimate (not proof of sensor wear)	Danne 2017
`calculateAGPMetrics`	Mean, SD, CV, LBGI/HBGI, ADRR, GRADE, GRI, J-index, MODD, CONGA, active percent, and reading count	Convenience aggregate

Series & events

Function	What it computes
`buildAGPProfile`	AGP-style time-of-day percentile-band series (5/25/50/75/95)
`analyzeMealResponse`	Baseline, peak, delta, time-to-peak, return-to-baseline, iAUC
`glucoseAUC` / `incrementalAUC`	Trapezoidal AUC; Wolever 4-case iAUC above baseline
`computeGlucoseTrend`	Rate-of-change + trend classification (Dexcom-style thresholds)
`detectGaps` / `splitDayNight` / `alignToGrid`	Sensor gaps, nocturnal split, uniform-grid resampling
`aggregateCohort`	Population distributions of TIR/GMI/CV/mean across patients

Reading-based APIs such as calculateADRR, calculateGVIPGS, glucoseMAG, and glucoseGVP inspect the unit on every GlucoseReading and can normalize a mixed-unit series. calculateGVIPGS therefore takes unit-bearing readings, not a numeric array plus a unit option.

Numeric-array APIs require one homogeneous unit. glucoseLBGI, glucoseHBGI, and calculateJIndex take the unit as a positional argument; calculateGRADE takes it as the fourth positional argument; and glucoseMValue and calculateIGC take it in their options. glucoseMAGE operates on one homogeneous numeric scale and has no unit option. Always follow the individual signature before removing unit metadata.

Glucose IQ score

glucoseIQScore is a project-defined, non-diagnostic wellness heuristic derived from GRI. It is not a validated clinical score and must not be used for diagnosis or treatment decisions.