ImputeLongiCovs: Longitudinal Imputation of Categorical Variables via a Joint Transition Model

Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.

Version: 0.1.0
Depends: R (≥ 3.4.4)
Imports: nnet, stats
Suggests: knitr, rmarkdown
Published: 2023-10-06
Author: Pavlos Mamouris [aut, cre], Vahid Nassiri [aut, ctb], Geert Molenberghs [ctb], Geert Verbeke [ctb]
Maintainer: Pavlos Mamouris <pavlos.k.mamouris at gmail.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: ImputeLongiCovs results

Documentation:

Reference manual: ImputeLongiCovs.pdf
Vignettes: Title of your vignette

Downloads:

Package source: ImputeLongiCovs_0.1.0.tar.gz
Windows binaries: r-prerel: ImputeLongiCovs_0.1.0.zip, r-release: ImputeLongiCovs_0.1.0.zip, r-oldrel: ImputeLongiCovs_0.1.0.zip
macOS binaries: r-prerel (arm64): ImputeLongiCovs_0.1.0.tgz, r-release (arm64): ImputeLongiCovs_0.1.0.tgz, r-oldrel (arm64): ImputeLongiCovs_0.1.0.tgz, r-prerel (x86_64): ImputeLongiCovs_0.1.0.tgz, r-release (x86_64): ImputeLongiCovs_0.1.0.tgz

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