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This repository contains a collection of structural equation models for the OpenMx [1,2] software package in R [3]. The models provided in this repository are based on the models I use during my research and are accompanied by detailed examples on how to use the models and interpret the output. The contents of the repository is licensed under CC BY 4.0; if you make use of these models in your own research, please refer back to this GitHub repository or cite:

Teeuw, J. "A repository of OpenMx structural equation models", GitHub, release 2020-08-25,https://github.com/jalmar/openmx-models;

You need a working version of R statistical software (available for download fromr-project.org) with the OpenMx structural equation modelling sofware package (available for download fromopenmx.ssri.psu.edu) installed on your computer to run the OpenMx models. See the examples provided with the models for more details on how to use them in your own research.

Themeasurement model can be used to obtain a reliable estimate of a construct and can reveal the "true" association between two constructs in the absence of random error in the measurements. The utility of this model was empirically evaluated for the association between functional connectivity from resting-state functional MRI and various traits in theHuman Connectome Project Young Adult cohort (seeTeeuw et al., in review [4]).

• Themeasurement model for twin studies is a special case of acommon pathway model and can be used to estimate the heritability of the reliable component obtained through a measurement model. This model was used to estimate the heritability of the reliable and stable component of resting-state functional connectivity in a longitudinal adolescent twin study (seeTeeuw et al., NeuroImage 2019 [5]) and in theHuman Connectome Project Young Adult cohort (seeTeeuw et al., in review [4]).
• TheCholesky decomposition model for twin studies can be used to estimate the genetic and environmental contributions to a trait or the genetic and environmental correlation between traits.
• TheLongitudinal Cholesky decomposition model for twin studies is a special case of the Cholesky decomposition model that makes use of longitudinal repeated measures of the same trait(s), and can be used to estimate theheritability of age-related change rates of a trait or thechange in heritability of the trait with age.

 This work is licensed under aCreative Commons Attribution 4.0 International License.

This work was supported by the Consortium on Individual Development (CID;individualdevelopment.nl) and Biobanking and BioMolecular resources Research Infrastructure in The Netherlands (BBMRI-NL 2.0;bbmri.nl).

1. Neale, M.C., Hunter, M.D., Pritikin, J.N., Zahery, M., Brick, T.R., Kirkpatrick, R.M., Estabrook, R., Bates, T.C., Maes, H.H., Boker, S.M. (2016). “OpenMx 2.0: Extended structural equation and statistical modeling”. Psychometrika, vol. 81(2), p. 535–549.doi:10.1007/s11336-014-9435-8.
2. OpenMx structural equation modelling package for the R statistical framework; website:https://openmx.ssri.psu.edu
3. The R Project for Statistical Computing; website:https://www.r-project.org
4. Teeuw, J., Boomsma, D.I., Hulshoff Pol, H.E., Brouwer, R.M. (in review). “Reliability modelling of resting-state functional connectivity”.
5. Teeuw, J., Brouwer, R.M., Guimarães, J.P.O.F.T., Brandner, P., Koenis, M.M.G., Swagerman, S.C., Verwoert, M., Boomsma, D.I., Hulshoff Pol, H.E. (2019). “Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls”. NeuroImage, vol. 202, 116073.doi:10.1016/j.neuroimage.2019.116073