WeMix: Weighted Mixed-Effects Models Using Multilevel Pseudo Maximum Likelihood Estimation

Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels.

Version: 3.0.0
Depends: lme4, R (≥ 3.3.0)
Imports: numDeriv, statmod, Rmpfr, NPflow, Matrix, methods, minqa
Suggests: testthat, knitr, rmarkdown, EdSurvey
Published: 2019-03-30
Author: Paul Bailey, Claire Kelley, Trang Nguyen, Huade Huo.
Maintainer: Claire Kelley <ckelley at air.org>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
CRAN checks: WeMix results

Downloads:

Reference manual: WeMix.pdf
Vignettes: Introduction to Weighted Mixed-Effects Models With WeMix
Weighted Linear Mixed-Effects Models
Package source: WeMix_3.0.0.tar.gz
Windows binaries: r-devel: WeMix_3.0.0.zip, r-release: WeMix_3.0.0.zip, r-oldrel: WeMix_3.0.0.zip
OS X binaries: r-release: WeMix_3.0.0.tgz, r-oldrel: WeMix_3.0.0.tgz
Old sources: WeMix archive

Reverse dependencies:

Reverse imports: EdSurvey

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