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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: | 1.0.0 |
Depends: | R (≥ 2.10) |
Imports: | dplyr, ggplot2, rlang, stats, methods, scales |
Suggests: | INLA, knitr, testthat (≥ 3.0.0), tibble, rmarkdown, spelling |
Published: | 2024-06-24 |
DOI: | 10.32614/CRAN.package.inlamemi |
Author: | Emma Skarstein |
Maintainer: | Emma Skarstein <emma at skarstein.no> |
License: | MIT + file LICENSE |
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 |
Package source: | inlamemi_1.0.0.tar.gz |
Windows binaries: | r-devel: inlamemi_1.0.0.zip, r-release: inlamemi_1.0.0.zip, r-oldrel: not available |
macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): inlamemi_1.0.0.tgz, r-oldrel (x86_64): inlamemi_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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