REMLA: An R package for robust expectation-maximization estimation for latent variable models

Abstract

Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation–maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models.