The WeMix package estimates mixed-effects models (also called multilevel models, mixed models, or HLMs) with survey weights.

Usage,NULL

This package is unique in allowing users to analyze data that may have unequal selection
probability at both the individual and group
levels. For linear models, the model is evaluated with a weighted version of the estimating equations
used by Bates, Maechler, Bolker, and Walker (2015) in `lme4`

. In the non-linear case, WeMix uses numerical
integration (Gauss-Hermite and adaptive Gauss-Hermite quadrature) to estimate mixed-effects models with
survey weights at all levels of the model.
Note that `lme4`

is the preferred way to estimate such
models when there are no survey weights or weights only at the lowest level, and our
estimation starts with parameters estimated in lme4. WeMix is intended for use in cases
where there are weights at all levels and is only for use with fully nested data.
To start using WeMix, see the vignettes covering
the mathematical background of mixed-effects model estimation and use the
`mix`

function to estimate models. Use
`browseVignettes(package="WeMix")`

to see the vignettes.

Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01

Rabe-Hesketh, S., & Skrondal, A. (2006) Multilevel Modelling of Complex Survey Data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169, 805-827. https://doi.org/10.1111/j.1467-985X.2006.00426.x

Bates, D. & Pinheiro, J. C. (1998). Computational Methods for Multilevel Modelling. Bell labs working paper.