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 emory.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
SystemRequirements:	MOSEK (>= 6), MOSEK License (>= 6)
CRAN checks:	lincom results

Documentation:

Reference manual:	lincom.html , lincom.pdf
Vignettes:	Linear Biomarker Combination: Empirical Performance Optimization (source, R code)

Downloads:

Package source:	lincom_1.2.tar.gz
Windows binaries:	r-devel: lincom_1.2.zip, r-release: lincom_1.2.zip, r-oldrel: lincom_1.2.zip
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

Linking:

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