TwoCutoff: 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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
dplyr, mixtools, stats, pROC, xgboost, ggplot2, caret, patchwork, gridExtra |
| Suggests: |
rmarkdown, DiagrammeR, knitr, devtools |
| Published: |
2026-06-24 |
| DOI: |
10.32614/CRAN.package.TwoCutoff (may not be active yet) |
| Author: |
Bhrigu Kumar Rajbongshi [aut, cre, ctb],
Seyed Ehsan Saffari [aut, ctb],
Nastaran Marzban [aut, ctb] |
| Maintainer: |
Bhrigu Kumar Rajbongshi <kumarbhrigu536 at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
TwoCutoff results |
Documentation:
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