Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <doi:10.48550/arXiv.2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.
Version: | 1.0.2 |
Depends: | R (≥ 2.10) |
Imports: | JointAI, rjags, coda, foreach, data.table, future, doFuture, mathjaxr, survival, ggplot2, ordinal, progressr, Matrix, mcmcse |
Suggests: | knitr, rmarkdown, bookdown, R.rsp, ggpubr, testthat (≥ 3.0.0), spelling |
Published: | 2022-11-18 |
DOI: | 10.32614/CRAN.package.remiod |
Author: | Ying Liu [aut], Tony Wang [aut, cre] |
Maintainer: | Tony Wang <xwang at imedacs.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/xsswang/remiod |
NeedsCompilation: | no |
SystemRequirements: | JAGS (http://mcmc-jags.sourceforge.net/) |
Language: | en-US |
Materials: | README NEWS |
CRAN checks: | remiod results |
Reference manual: | remiod.pdf |
Vignettes: |
Example: Binary data imputation Example: Continuous data imputation through GLM Introduction to remiod |
Package source: | remiod_1.0.2.tar.gz |
Windows binaries: | r-devel: remiod_1.0.2.zip, r-release: remiod_1.0.2.zip, r-oldrel: remiod_1.0.2.zip |
macOS binaries: | r-release (arm64): remiod_1.0.2.tgz, r-oldrel (arm64): remiod_1.0.2.tgz, r-release (x86_64): remiod_1.0.2.tgz, r-oldrel (x86_64): remiod_1.0.2.tgz |
Old sources: | remiod archive |
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