remiod: Reference-Based Multiple Imputation for Ordinal/Binary Response

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <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: 1.0.1
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: 2022-09-05
Author: Ying Liu [aut], Tony Wang ORCID iD [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

Documentation:

Reference manual: remiod.pdf
Vignettes: Example: Binary Data Imputation
Example: Continuous Data Imputation through GLM
Introduction to remiod

Downloads:

Package source: remiod_1.0.1.tar.gz
Windows binaries: r-devel: remiod_1.0.1.zip, r-release: remiod_1.0.1.zip, r-oldrel: remiod_1.0.1.zip
macOS binaries: r-release (arm64): remiod_1.0.1.tgz, r-oldrel (arm64): remiod_1.0.1.tgz, r-release (x86_64): remiod_1.0.1.tgz, r-oldrel (x86_64): remiod_1.0.1.tgz
Old sources: remiod archive

Linking:

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