Deriving Clinically Interpretable Cutoffs for Disease Biomarkers // Provides a reproducible pipeline for deriving two clinically meaningful cutoffs for disease biomarkers using a unified two- stage framework. The package integrates finite mixture modeling with risk prediction using biomarker plus clinical features, followed by decision curve analysis to evaluate clinical utility. Outputs include biomarker density plots, risk calibration curves, decision curves, and summary tables of diagnostic performance. Designed for researchers in bio- statistics, neurology, and data science, this package emphasizes reproducibility, transparency, and clear clinical relevance.