Installation | Simulated dataset for illustration | Empirical maximization of specificity at controlled sensitivity (or sensitivity at controlled specificity) | Empirical minimization of weighted average of false positive rate and false negative rate | References
Package: lincom 1.2
Yijian Huang
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.
Authors:
lincom_1.2.tar.gz
lincom_1.2.tar.gz(r-4.7-arm64)lincom_1.2.tar.gz(r-4.7-x86_64)lincom_1.2.tar.gz(r-4.6-arm64)lincom_1.2.tar.gz(r-4.6-x86_64)
lincom_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lincom/json (API)
| # Install 'lincom' in R: |
| install.packages('lincom', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:61ee036498. Checks:6 OK. Indexed: yes.
