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rebound_events() to detect rebound hypoglycemia
and rebound hyperglycemia using cgmguru Level 1 initial events followed
by an opposite threshold crossing within 120 minutes. The function
returns the preprocessed event grid by default.
detect_all_events() includes rebound rows and wide summary
columns.summary_digits to
detect_all_events() to control rounding for numeric summary
outputs. The default remains 2 decimal places; use NULL or
"none" to return unrounded values.detect_all_events() documentation so formulas
involving mean_glucose render the column name literally
instead of treating the underscore as a subscript in Rd output.conga_rcpp() for iglu-compatible
Continuous Overall Net Glycemic Action (CONGA) calculation. The function
interpolates CGM data to a regular day-aligned grid and returns the
standard deviation of glucose differences separated by n
hours.mage_rcpp() for iglu-compatible Mean
Amplitude of Glycemic Excursions (MAGE) calculation. The function
supports the moving-average (version = "ma") and legacy
standard-deviation (version = "naive") algorithms,
segment-level output with return_type = "df", and
avg/service/max/plus/minus
direction summaries. Plotting options from iglu::mage() are
intentionally not implemented.modd_rcpp() for iglu-compatible Mean
of Daily Differences (MODD) calculation. The function uses cgmguru’s
iglu-compatible day-grid preprocessing and returns same-time-of-day
absolute glucose differences averaged across the requested day
lag.excursion() episode-start output to include the
peak glucose value within 2 hours of the excursion start
(maxima_glucose), its time (maxima_time), time
to peak in minutes (time_to_peak_min), and
maxima_index. Excursion starts now require both the
previous glucose value and the starting glucose value to be at least 70
mg/dL before checking for a rise greater than 70 mg/dL within 2
hours.conga_rcpp(),
mage_rcpp(), and modd_rcpp() using the iglu
example CGM datasets.sensor_wear() tests to avoid timezone-dependent
one-to-one start_date comparisons against
iglu::active_percent() manual windows. Fixed-window sensor
wear tests now compare the calculated observed/expected reading counts
directly, making the checks stable across DST-sensitive timezones.cgmguru vignette into a
practical CGM analysis guide covering data requirements, sensor wear,
event summaries, event-grid inspection, GRID analysis, postprandial
maxima workflows, excursions, visualization, and scaling to larger
datasets.maxima_grid() and
detect_between_maxima() to include all subject IDs in
episode_counts, returning 0 for subjects with
no detected episodes or between-maxima results.detect_all_events() to summarize event counts
segment-by-segment after interpolation gaps, preventing events that end
at a gap boundary from being merged into the next segment.return_interpolated = FALSE, improving speed and
memory use for calls that do not request the interpolated data.detect_all_events() summary glucose metrics to
use original raw CGM values by default, with
summary_metrics_source = "preprocessed" for the previous
internal event-grid behavior.detect_all_events() CGM summary metrics and
sensor wear outputs to two decimal places.sensor_wear_ndays to
detect_all_events() to calculate
sensor_wear_percent over a fixed retrospective window, such
as the last 90 days; when omitted, sensor_wear_percent
continues to use the original timestamp span.sensor_wear() so the default calculation uses
each subject’s original timestamp span. Supplying ndays now
switches to the fixed-window calculation.detect_all_events() return tables to
subject_summary and
glycemic_event_summary.detect_all_events() summary columns for
clarity: sensor_wear_percent,
*_total_episodes, and
avg_minutes_below_54_per_episode; CV is now
reported as a percent.total_episodes
for standalone hypo-/hyperglycemic event summaries and
detect_all_events() long-format event output.total_episodes consistently.inter_gap, gap masking, and
segment-wise event classification.interpolate_cgm() as a standalone helper for
inspecting the interpolated event grid used by glycemic event
functions.sensor_wear() and included observed-data sensor
wear in detect_all_events() summary output.detect_all_events() to calculate CGM summary
metrics on the interpolated event grid while returning event and summary
tables only.type = "lv1",
"lv2", and "extended" to
detect_hyperglycemic_events() and
detect_hypoglycemic_events().end_glucose and
end_index identify the final dysglycemic reading
immediately before the confirmed recovery period begins.indices to index, start_indices
to start_index, end_indices to
end_index, max_indices to
max_index, and min_indices to
min_index.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.