PLmixed: Estimate (Generalized) Linear Mixed Models with Factor Structures

Utilizes the 'lme4' and 'optimx' packages (previously 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) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>. 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.

Version:	0.1.6
Depends:	R (≥ 3.2.2)
Imports:	lme4, Matrix (≥ 1.1.1), numDeriv, stats, optimx
Suggests:	knitr, rmarkdown, irtoys
Published:	2022-06-22
Author:	Minjeong Jeon [aut], Nicholas Rockwood [aut, cre]
Maintainer:	Nicholas Rockwood <njrockwood at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	PLmixed citation info
Materials:	README
In views:	Psychometrics
CRAN checks:	PLmixed results

Documentation:

Reference manual:	PLmixed.pdf
Vignettes:	PLmixed: An Introduction

Downloads:

Package source:	PLmixed_0.1.6.tar.gz
Windows binaries:	r-devel: PLmixed_0.1.6.zip, r-release: PLmixed_0.1.6.zip, r-oldrel: PLmixed_0.1.6.zip
macOS binaries:	r-release (arm64): PLmixed_0.1.6.tgz, r-oldrel (arm64): PLmixed_0.1.6.tgz, r-release (x86_64): PLmixed_0.1.6.tgz, r-oldrel (x86_64): PLmixed_0.1.6.tgz
Old sources:	PLmixed archive

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