Estimate (Generalized) Linear Mixed Models with Factor
Structures

Utilizes the 'lme4' package and the optim() function from 'stats'
to estimate (generalized) linear mixed models (GLMM) with factor
structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012) .
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.

The purpose of PLmixed is to extend the capabilities of lme4 to allow factor structures (i.e., factor loadings and discrimination parameters) to be freely estimated. Thus, factor analysis and item response theory models with multiple hierarchical levels and/or crossed random effects can be estimated using code that requires little more input than that required by lme4. All of the strengths of lme4, including the ability to add (possibly random) covariates and an arbitrary number of crossed random effects, are encompassed within PLmixed. In fact, PLmixed uses lme4 and optim to estimate the model using nested maximizations. Details of this approach can be found in Jeon and Rabe-Hesketh (2012). A manuscript documenting the use of PLmixed is currently in preparation.