OpenMx

OpenMx: an open source extended structural equation modeling framework. OpenMx is free, full-featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS-X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced -- these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.


References in zbMATH (referenced in 36 articles , 2 standard articles )

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  1. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  2. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  3. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  4. Wang, Jichuan; Wang, Xiaoqian: Structural equation modeling. Applications using Mplus (2020)
  5. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  6. Hu, Yueqin; Treinen, Raymond: A one-step method for modelling longitudinal data with differential equations (2019)
  7. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  8. Noh, Maengseok; Lee, Youngjo; Oud, Johan H. L.; Toharudin, Toni: Hierarchical likelihood approach to non-Gaussian factor analysis (2019)
  9. Usami, Satoshi; Jacobucci, Ross; Hayes, Timothy: The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories (2019)
  10. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  11. Snoke, Joshua; Brick, Timothy R.; Slavković, Aleksandra; Hunter, Michael D.: Providing accurate models across private partitioned data: secure maximum likelihood estimation (2018)
  12. Charles Driver and Johan Oud and Manuel Voelkle: Continuous Time Structural Equation Modeling with R Package ctsem (2017) not zbMATH
  13. Epskamp, Sacha; Rhemtulla, Mijke; Borsboom, Denny: Generalized network psychometrics: combining network and latent variable models (2017)
  14. Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne: Growth modeling. Structural equation and multilevel modeling approaches (2017)
  15. Inga Schwabe: BayesTwin: An R Package for Bayesian Inference of Item-Level Twin Data (2017) not zbMATH
  16. Nancy, Jane Y.; Khanna, Nehemiah H.; Arputharaj, Kannan: Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework (2017)
  17. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017) not zbMATH
  18. Oleg Sofrygin; Mark van der Laan; Romain Neugebauer: simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data (2017) not zbMATH
  19. de Zeeuw, Eveline L.; van Beijsterveldt, Catharina E. M.; Glasner, Tina J.; de Geus, Eco J. C.; Boomsma, Dorret I.: Arithmetic, reading and writing performance has a strong genetic component: a study in primary school children (2016) MathEduc
  20. Gu, Fei; Wu, Hao: Raw data maximum likelihood estimation for common principal component models: a state space approach (2016)

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