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run_app() example from \dontrun{} to
if (interactive()) { ... }.ipeb() and ipeb_run() accept an optional
seed argument that fixes the internal validation split,
making a fit exactly reproducible.ipeb() fits the improved Parametric Empirical Bayes
model on training data: a time-gap-aware standardization layer (random
intercept, optional random slope, optional AR(1)/OU residual
autocorrelation), optional covariate adjustment, objective-driven
weighting (sensitivity, lead-time, or combined objective), optional
feature selection, and an automatically chosen scalar or multivariate
combiner.predict() scores new subjects, and
evaluate() reports per-patient sensitivity and lead time
with per-visit specificity at user-chosen operating points.ipeb_run() provides a one-call fit-and-evaluate
wrapper.ipeb_innovations() exposes the time-gap-aware
standardization layer, so history-adjusted baselines can be built from
the same layer as iPEB.print(), summary(), and
plot() methods for fitted ipeb objects.ipeb_example.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.