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 models. 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. The model is fit using adaptive quadrature following the methodology of Rabe-Hesketh, S., and Skrondal, A. (2006) <doi:10.1111/j.1467-985X.2006.00426.x>.

Version: 1.0.2
Depends: lme4, R (≥ 3.2.0)
Imports: numDeriv, minqa, statmod, Rmpfr, MASS, NPflow, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, EdSurvey
Published: 2018-04-10
Author: Paul Bailey, Claire Kelley, Trang Nguyen, Huade Huo.
Maintainer: Claire Kelley <ckelley at air.org>
License: GPL-2
NeedsCompilation: yes
Materials: README
CRAN checks: WeMix results


Reference manual: WeMix.pdf
Vignettes: WeMix Vignettes
Package source: WeMix_1.0.2.tar.gz
Windows binaries: r-devel: WeMix_1.0.2.zip, r-release: WeMix_1.0.2.zip, r-oldrel: WeMix_1.0.2.zip
OS X binaries: r-release: WeMix_1.0.2.tgz, r-oldrel: WeMix_1.0.2.tgz


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