doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.">

inlamemi: Missing Data and Measurement Error Modelling in INLA (original) (raw)

Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.

Version: 1.1.0
Depends: R (≥ 2.10)
Imports: dplyr, ggplot2, rlang, stats, methods, scales
Suggests: INLA, knitr, testthat (≥ 3.0.0), tibble, rmarkdown, spelling
Published: 2024-10-31
DOI: 10.32614/CRAN.package.inlamemi
Author: Emma Skarstein ORCID iD [cre, aut, cph], Stefanie Muff ORCID iD [aut]
Maintainer: Emma Skarstein
License: MIT + file
URL: https://emmaskarstein.github.io/inlamemi/,https://github.com/emmaSkarstein/inlamemi
NeedsCompilation: no
Additional_repositories: https://inla.r-inla-download.org/R/stable/
Language: en-US
Citation: inlamemi citation info
Materials: README, NEWS
CRAN checks: inlamemi results

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