Linear Latent Variable Models: The lava-package

TL;DR

The lava-package in R provides a flexible, modular framework for specifying and estimating complex linear latent variable models with advanced features like robust errors, multigroup analysis, and handling incomplete data.

Contribution

It introduces an R package that separates model specification from data, enabling dynamic modeling of hierarchical structures with numerous advanced features.

Findings

Demonstrated on human brain serotonin transporter data

Supports a broad range of non-linear structural equation models

Includes extensive simulation capabilities

Abstract

An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.