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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.
PLmixed can be installed from CRAN with:
install.packages("PLmixed")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.