1 Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, Københavns Universitet 2 Section IV. Building 22.4/24.4, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, Københavns Universitet 3 Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, Københavns Universitet
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. © 2012 Springer-Verlag.
Computational Statistics, 2013, Vol 28, Issue 4, p. 1385-1452
Latent variable model; Maximum likelihood; R; Seasonality; Serotonin; SERT; Structural equation model
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