The WeMix package estimates mixed-effects models (also called multilevel models, mixed models, or hierarchical linear models) with survey weights. The likelihood function of such models with complex survey weights is not analytically calculable, so 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.
This method allows users to analyze data that may have unequal selection probability at both the individual and group levels. 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.