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OpenMx is a free and open source software for use with R that allows estimation of a wide variety of advanced multivariate statistical models.
Statistical Software Free
• Multivariate Covariance Modeling • Covariance Modeling With Means • Full Information Maximum Likelihood • Missing Data • Categorical Threshold Estimation • Multiple Groups • Hierarchical Model Definition • Multicore Computers • Large Scale Distributed Processing
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
• Integration with R • Supports Popular Operating Systems • Open Source • Interactive Model Building • Model Modification • Flexible Reporting • Multivariate Covariance Modeling
What are the benefits?
• Mixed Effects • Matrix Algebra Calculations • Parameter Equality Constraints • Parameter Algebraic Constraints • Nonlinear Inequality Constraints • Procedural Model Specification • User Specified Functions for Model Specification • User Specified Objective Functions • Community Wiki and Forums
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OpenMx is a free open source software for fitting Structural Equation Models (SEM) to observed data and offers the features users would expect in an SEM software package.
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OpenMx is a free and open source software for use with R that allows estimation of a wide variety of advanced multivariate statistical models. OpenMx consists of features such as a library of functions and optimizers that allow users to quickly and flexibly define an SEM model and estimate parameters given observed data.
OpenMx runs on MacOS, Windows, and most varieties of Linux/GNU where the same scripts users write in Windows will run in MacOS or Linux. OpenMx can be used by those who think in terms of path models or by those who prefer to specify models in terms of matrix algebra.
OpenMx is extremely powerful, taking full advantage of the R programming environment where complicated models and data sets can be specified and modified using the R language. OpenMx returns the results of user’s model estimation directly back into R where users can use all of R's reporting functions such as generating tables or graphs of their model parameters or fit statistics which users can generate tables of comparisons of nested fit statistics all within a single script.
OpenMx estimates maximum likelihood parameters for models with multivariate outcomes given an observed covariance matrix where models can be specified to include latent variables. OpenMx also fits models to raw data using FIML.
OpenMx uses OpenMP and the R package Snow to allow the multiple CPU cores in the user’s computer to operate in parallel on a task. These features are built into all FIML algorithms (continuous, ordinal and joint) which can be especially CPU intensive.
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