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