Package: Mmcsd 1.0.0

Mmcsd: Modeling Complex Longitudinal Data in a Quick and Easy Way

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>.

Authors:Pedro Pacheco [aut, cre], Marcel Vieira [aut], Gustavo Silva [aut]

Mmcsd_1.0.0.tar.gz
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Mmcsd.pdf |Mmcsd.html✨
Mmcsd/json (API)
NEWS

# Install 'Mmcsd' in R:
install.packages('Mmcsd', repos = c('https://gustavo039.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

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

2.70 score 3 scripts 201 downloads 4 exports 30 dependencies

Last updated 2 years agofrom:facc01c8d7. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winWARNINGNov 21 2024
R-4.5-linuxWARNINGNov 21 2024
R-4.4-winWARNINGNov 21 2024
R-4.4-macWARNINGNov 21 2024
R-4.3-winWARNINGNov 21 2024
R-4.3-macWARNINGNov 21 2024

Exports:%>%cov_mmcsdmmcsdsigmaThetaExpr_viewer

Dependencies:clicpp11data.tabledplyrevaluatefansigenericsgluehighrjsonliteknitrlifecyclemagrittrpillarpkgconfigpurrrR6rlangrliststringistringrtibbletidyrtidyselectutf8vctrswithrxfunXMLyaml

Modeling complex longitudinal data in a quick and easy way

Rendered frommy-vignette.Rmdusingknitr::rmarkdownon Nov 21 2024.

Last update: 2023-03-31
Started: 2023-03-31