Linear Latent Variable Models: The lava-package
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.
Cite
@article{arxiv.1206.3421,
title = {Linear Latent Variable Models: The lava-package},
author = {Klaus K. Holst and Esben Budtz-Jørgensen},
journal= {arXiv preprint arXiv:1206.3421},
year = {2013}
}