Correcting Misclassified Binary Outcomes in Association Studies
Overview
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.
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- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
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- Help pages
- 48
- Examples that run
- 42%5 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran) · 3 never run
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 4%
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Maintainer since 1.0.0 (2023)
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
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- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.0.02023-04-19
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-19
- Total releases
- 3 / 3 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.2.0
- Bundled data
- 289 KB / 3 files
- Download size
- 363 KB
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