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 | Other |
| 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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