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 expectationmaximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.
Package details 


Author  Bryan OrtizTorres [aut, cre], Kenneth Nieser [aut] (<https://orcid.org/0000000160031296>) 
Maintainer  Bryan OrtizTorres <bortiztorres@wisc.edu> 
License  GPL (>= 3) 
Version  1.1 
URL  https://github.com/knieser/REM 
Package repository  View on CRAN 
Installation 
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