Journal
COMPUTATIONAL STATISTICS
Volume 28, Issue 4, Pages 1385-1452Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-012-0344-y
Keywords
Latent variable model; Structural equation model; R; Maximum likelihood; Serotonin; Seasonality; SERT
Categories
Funding
- Danish Agency for Science, Technology and Innovation
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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.
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