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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: | 1.1 |
Depends: | R (≥ 4.0), GPArotation, geex |
Imports: | stats |
Suggests: | knitr, lavaan, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2024-05-11 |
DOI: | 10.32614/CRAN.package.REMLA |
Author: | Bryan Ortiz-Torres [aut, cre],
Kenneth Nieser |
Maintainer: | Bryan Ortiz-Torres <bortiztorres at wisc.edu> |
License: | GPL (≥ 3) |
URL: | https://github.com/knieser/REM |
NeedsCompilation: | no |
CRAN checks: | REMLA results |
Reference manual: | REMLA.pdf |
Vignettes: |
REM_tutorial |
Package source: | REMLA_1.1.tar.gz |
Windows binaries: | r-devel: REMLA_1.1.zip, r-release: REMLA_1.1.zip, r-oldrel: REMLA_1.1.zip |
macOS binaries: | r-release (arm64): REMLA_1.1.tgz, r-oldrel (arm64): REMLA_1.1.tgz, r-release (x86_64): REMLA_1.1.tgz, r-oldrel (x86_64): REMLA_1.1.tgz |
Old sources: | REMLA archive |
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These binaries (installable software) and packages are in development.
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