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: