REMLA: Robust Expectation-Maximization Estimation for Latent Variable Models

Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.

Version:	1.0
Imports:	GPArotation, stats, geex
Suggests:	knitr, lavaan, rmarkdown, testthat (≥ 3.0.0)
Published:	2024-03-26
Author:	Bryan Ortiz-Torres [aut, cre], Kenneth Nieser [aut]
Maintainer:	Bryan Ortiz-Torres <bortiztorres at wisc.edu>
License:	GPL (≥ 3)
URL:	https://github.com/knieser/REM
NeedsCompilation:	no
CRAN checks:	REMLA results

Documentation:

Reference manual:	REMLA.pdf
Vignettes:	REM_tutorial

Downloads:

Package source:	REMLA_1.0.tar.gz
Windows binaries:	r-devel: REMLA_1.0.zip, r-release: REMLA_1.0.zip, r-oldrel: REMLA_1.0.zip
macOS binaries:	r-release (arm64): REMLA_1.0.tgz, r-oldrel (arm64): REMLA_1.0.tgz, r-release (x86_64): REMLA_1.0.tgz, r-oldrel (x86_64): REMLA_1.0.tgz

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