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")
```

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