Package: ImputeLongiCovs 0.1.0

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.

Authors:Pavlos Mamouris [aut, cre], Vahid Nassiri [aut, ctb], Geert Molenberghs [ctb], Geert Verbeke [ctb]

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# Install 'ImputeLongiCovs' in R:
install.packages('ImputeLongiCovs', repos = c('https://pmamouris.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • analyses_data - Analyses Data for imputing categorical covariates
  • initial_data - Initial Data for imputing categorical covariates

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2 exports 0.09 score 1 dependencies 2 scripts 150 downloads

Last updated 12 months agofrom:be25a6c63e. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 24 2024
R-4.5-winNOTEAug 24 2024
R-4.5-linuxNOTEAug 24 2024
R-4.4-winNOTEAug 24 2024
R-4.4-macNOTEAug 24 2024
R-4.3-winOKAug 24 2024
R-4.3-macOKAug 24 2024

Exports:create_probMatriximpute_categorical_covariates

Dependencies:nnet

Impute longitudinal categorical covariates in R using the ImputeLongiCovs package

Rendered fromvignette_theory.Rmdusingknitr::rmarkdownon Aug 24 2024.

Last update: 2023-10-06
Started: 2023-10-06