Package: iPEB 0.1.1

iPEB: Improved Parametric Empirical Bayes for Longitudinal Biomarker Analysis

Extends parametric empirical Bayes (PEB) for longitudinal biomarker screening with a time-gap-aware standardization layer, covariate adjustment, and objective-driven multi-marker weighting. The layer models each subject's biomarker history with a random intercept (and an optional random slope) and autocorrelated, gap-scaled residuals, so that prediction uncertainty grows with the time between visits and per-visit specificity is preserved under irregular sampling. Marker weights are learned to optimize a user-selected clinical objective -- maximizing sensitivity at a fixed specificity, extending detection lead time, or a combined objective -- with optional feature selection and a choice of scalar or multivariate combiner. Functions for fitting, prediction, and evaluation (sensitivity, lead time, and specificity at chosen operating points) are provided. A manuscript describing the method is in preparation.

Authors:Bitan Sarkar [aut, cre, cph], Ana Maria Kenney [aut], James P. Long [aut], Johannes F. Fahrmann [aut], Samir Hanash [aut], Kim-Anh Do [aut], Ehsan Irajizad [aut]