OpenMx 2.0: Extended Structural Equation and Statistical Modeling
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The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.
Keywordsstructural equation modeling path analysis item factor analysis state space modeling mixture distribution latent class analysis optimization big data time series behavior genetics substance use data analysis full information maximum likelihood ordinal data
The authors gratefully acknowledge funding from the National Institutes of Health, specifically Grants R01-DA022989 (PI Boker), R37-DA018673 and R25-DA026119 (PI Neale). Thanks are also due to a large group of beta-testers, including but not limited to: Mike W.-L. Cheung (2014), Charles Driver, Dorothy Bishop, Greg Carey, Pascal Deboeck, Emilio Ferrer, Christopher Hertzog, Kevin Grimm, Ken Kelley, Matthew Keller, Jean-Philippe Laurenceau, Gitta Lubke, John J. McArdle, Sam McQuillin, Sarah Medland, William Revelle, Michael Scharkow, James Steiger, Melissa Sturge-Apple, and Theodore Walls.
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