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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: | 1.1 |
| Depends: | R (≥ 3.6.0) |
| Imports: | SparseM, Matrix, Rmosek, methods, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2023-06-21 |
| 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_1.1.tar.gz |
| Windows binaries: | r-devel: lincom_1.1.zip, r-release: lincom_1.1.zip, r-oldrel: lincom_1.1.zip |
| macOS binaries: | r-release (arm64): lincom_1.1.tgz, r-oldrel (arm64): lincom_1.1.tgz, r-release (x86_64): lincom_1.1.tgz, r-oldrel (x86_64): lincom_1.1.tgz |
| Old sources: | lincom archive |
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