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lcc: Longitudinal Concordance Correlation

Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>. In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.

Version: 1.1.4
Depends: R (≥ 3.2.3), nlme (≥ 3.1-124), ggplot2 (≥ 2.2.1)
Imports: hnp, parallel, doSNOW, doRNG, foreach
Suggests: roxygen2 (≥ 3.0.0), covr, testthat, MASS
Published: 2022-08-25
DOI: 10.32614/CRAN.package.lcc
Author: Thiago de Paula Oliveira ORCID iD [aut, cre], Rafael de Andrade Moral ORCID iD [aut], Silvio Sandoval Zocchi ORCID iD [ctb], Clarice Garcia Borges Demetrio ORCID iD [ctb], John Hinde ORCID iD [aut]
Maintainer: Thiago de Paula Oliveira <thiago.paula.oliveira at alumni.usp.br>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: lcc citation info
CRAN checks: lcc results

Documentation:

Reference manual: lcc.pdf

Downloads:

Package source: lcc_1.1.4.tar.gz
Windows binaries: r-devel: lcc_1.1.4.zip, r-release: lcc_1.1.4.zip, r-oldrel: lcc_1.1.4.zip
macOS binaries: r-release (arm64): lcc_1.1.4.tgz, r-oldrel (arm64): lcc_1.1.4.tgz, r-release (x86_64): lcc_1.1.4.tgz, r-oldrel (x86_64): lcc_1.1.4.tgz
Old sources: lcc archive

Linking:

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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.