Linear latent variable models: the lava-package

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Paper: Linear latent variable models: the lava-package

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.

Citation

@Article{lava,
  Title =	 {Linear latent variable models: the lava-package},
  Author =	 {Holst, Klaus K and Budtz-Jørgensen, Esben},
  Journal =	 {Computational Statistics},
  Year =	 2013,
  Number =	 4,
  Pages =	 {1385--1452},
  Volume =	 28,
  Doi =		 {10.1007/s00180-012-0344-y},
  Publisher =	 {Springer Berlin Heidelberg}
}