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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.