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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 correct outcome classification occurs in at least 50% of observations. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.
| Version: | 1.0.0 |
| 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), kableExtra (≥ 1.3.4) |
| Published: | 2023-04-19 |
| Author: | Kimberly Hochstedler [aut, cre] |
| Maintainer: | Kimberly Hochstedler <kah343 at cornell.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | COMBO results |
| Reference manual: | COMBO.pdf |
| Vignettes: |
COMBO Notation Guide COMBO Notation Guide - Two-stage Misclassification Model |
| Package source: | COMBO_1.0.0.tar.gz |
| Windows binaries: | r-devel: COMBO_1.0.0.zip, r-release: COMBO_1.0.0.zip, r-oldrel: COMBO_1.0.0.zip |
| macOS binaries: | r-release (arm64): COMBO_1.0.0.tgz, r-oldrel (arm64): COMBO_1.0.0.tgz, r-release (x86_64): COMBO_1.0.0.tgz, r-oldrel (x86_64): COMBO_1.0.0.tgz |
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