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>.
Version: 1.2.0 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) Published: 2024-10-30 DOI: 10.32614/CRAN.package.COMBO Author: Kimberly Hochstedler Webb [aut, cre] Maintainer: Kimberly Hochstedler Webb <kah343 at cornell.edu> License: MIT + file LICENSE NeedsCompilation: no SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net) Materials: README CRAN checks: COMBO results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=COMBO to link to this page.
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