Linear Latent Variable Models: The lava-package

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Authors Klaus K. Holst, Esben Budtz-Jørgensen
Journal/Conference Name Computational Statistics
Paper Category
Paper 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.
Date of publication 2013
Code Programming Language R

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