Linear latent variable models: the lava-package. 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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References in zbMATH (referenced in 3 articles , 1 standard article )
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- Brehm, C.; Hader, C.; Fasel, H. F.: A locally stabilized immersed boundary method for the compressible Navier-Stokes equations (2015)
- Eriksson, Frank; Gerds, Thomas Alexander; Lesaffre, Emmanuel: Unobserved confounder effects in models for clustered dental failure time data (2014)
- Holst, Klaus Kähler; Budtz-Jørgensen, Esben: Linear latent variable models: the lava-package (2013)