# PLmixed

The purpose of `PLmixed`

is to extend the capabilities of the `R`

package `lme4`

(Bates, Machler, Bolker, & Walker, 2015) to allow factor structures (i.e., factor loadings, weights, 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) doi:10.3102/1076998611417628. A manuscript documenting the use of `PLmixed`

is currently in preparation.

## Citations:

Rockwood, N. J. & Jeon, M. (2019). Estimating complex measurement and growth models using the R package PLmixed. Multivariate Behavioral Research. [link]Jeon, M. & Rockwood, N. J. (2017). PLmixed: Estimate (Generalized) Linear Mixed Models with Factor Structures. R package version 0.1.0. https://CRAN.R-project.org/package=PLmixed.

## Installation

You can install PLmixed from CRAN in `R`

with:

```
install.packages("PLmixed")
```

## Examples

A vignette containing two example analyses can be found here.