OpenMx 2.0: Extended Structural Equation and Statistical Modeling

By Michael C. Neale, Michael D. Hunter, Joshua N. Pritikin, Mahsa Zahery, Timothy R. Brick, R. Kirkpatrick, R. Estabrook, Timothy C. Bates, H. Maes, Steven M. Boker. Psychometrika, 2015

995 citations recorded by the source corpus.

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.

Subfields: Applied Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9f43122051357c63766120d23c796a6ab13d4a51

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Citations in the corpus

Generalized Network Psychometrics: Combining Network and Latent Variable Models (2016, positive): “We have developed the R package lvnet6, which utilizes OpenMx (Neale et al., 2015) for confirmative testing of RNM and LNM models (as well as a combination of the two).”
Sample Size Recommendations for Continuous-Time Models: Compensating Shorter Time Series with Larger Numbers of Persons and Vice Versa (2020, methodology): “3OpenMx sometimes outputs no standard errors even when the exit code is 0.”
MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus (2018, methodology): “Several packages within the R language (R Core Team, 2015) provide excellent open-source tools for ﬁ tting structural equation models, including lavaan (Rosseel, 2012) and OpenMX (Neale et al., 2016).”
Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, methodology): “We used the R package Rsolnp (Ghalanos & Theussl, 2015) for optimization and lavaan to fit models M0 and M1 and to compute the difference tests.14 Although this optimization strategy is somewhat inefficient, it performed well and results for a subset of Study 1 were checked against OpenMx to ensure ”