Correcting Misclassified Binary Outcomes in Association Studies R package COMBO version 1.2.0 Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>. Software Depends: R (>= 4.2.0) Imports: dplyr (>= 1.0.10), tidyr (>= 1.2.1), Matrix (> 1.4-1), rjags (>= 4-13), turboEM (>= 2021.1), SAMBA (>= 0.9.0), utils (>= 4.2.0) Suggests: knitr (>= 1.40), testthat (>= 3.0.0), devtools (>= 2.4.5), xtable (>= 1.8.0) Kimberly Hochstedler Webb <kah343@cornell.edu> Comprehensive R Archive Network (CRAN) Kimberly Hochstedler Webb [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=COMBO/LICENSE) 2024-10-30 application/tgz https://CRAN.R-project.org/package=COMBO doi:10.32614/CRAN.package.COMBO