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Matching longitudinal methodology models with complex sampling design. It fits fixed and random effects models and covariance structured models so far. It also provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.
Version: | 1.0.0 |
Depends: | R (≥ 2.10) |
Imports: | dplyr, knitr, magrittr, methods, purrr, rlist, stats, tibble, tidyr |
Suggests: | rmarkdown, simstudy, kableExtra, tidyverse |
Published: | 2023-03-31 |
DOI: | 10.32614/CRAN.package.Mmcsd |
Author: | Pedro Pacheco |
Maintainer: | Pedro Pacheco <gustavoaeida2002 at gmail.com> |
License: | GPL (≥ 3) |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | Mmcsd results |
Reference manual: | Mmcsd.pdf |
Vignettes: |
Modeling complex longitudinal data in a quick and easy way |
Package source: | Mmcsd_1.0.0.tar.gz |
Windows binaries: | r-devel: Mmcsd_1.0.0.zip, r-release: Mmcsd_1.0.0.zip, r-oldrel: Mmcsd_1.0.0.zip |
macOS binaries: | r-release (arm64): Mmcsd_1.0.0.tgz, r-oldrel (arm64): Mmcsd_1.0.0.tgz, r-release (x86_64): Mmcsd_1.0.0.tgz, r-oldrel (x86_64): Mmcsd_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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