# 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)

- Version1.1
- R version≥ 4.0
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release05/11/2024

## Documentation

## Team

### Bryan Ortiz-Torres

### Kenneth Nieser

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- Depends3 packages
- Imports1 package
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