lincom: Linear Biomarker Combination: Empirical Performance Optimization

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free.

Version: 1.2
Depends: R (≥ 3.6.0)
Imports: SparseM, Rmosek, methods, stats
Suggests: knitr, rmarkdown
Published: 2024-06-30
DOI: 10.32614/CRAN.package.lincom
Author: Yijian Huang
Maintainer: Yijian Huang <yhuang5 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: MOSEK (>= 6), MOSEK License (>= 6)
CRAN checks: lincom results


Reference manual: lincom.pdf
Vignettes: Linear Biomarker Combination: Empirical Performance Optimization


Package source: lincom_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lincom_1.2.tgz, r-oldrel (arm64): lincom_1.2.tgz, r-release (x86_64): lincom_1.2.tgz, r-oldrel (x86_64): lincom_1.2.tgz
Old sources: lincom archive


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