OpenMx for R
OpenMx is free and open source software for use with R project that allows estimation of a wide variety of advanced multivariate statistical models. OpenMx consists of a library of functions and optimizers that allow you to quickly and flexibly define an SEM model and estimate parameters given observed data.
OpenMx runs on Mac OS X, Windows, and several varieties of Linux. This means the same scripts you write in Windows will run in Mac OS X 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. This means that complicated models and data sets can be specified and modified using the R language.
Click for the revised Introduction of the Documentation.
Mx: Statistical Modeling
*Mx is no longer in development. We recommend using OpenMX for R instead; it supersedes classic Mx.
Mx is a combination of a matrix algebra interpreter and a numerical optimizer. It enables exploration of matrix algebra through a variety of operations and functions. There are many built-in fit functions to enable structural equation modeling and other types of statistical modeling of data. It offers the fitting functions found in commercial software such as LISREL, LISCOMP, EQS and CALIS, along with facilities for maximum likelihood estimation of parameters from missing data structures, under normal theory. Complex ‘nonstandard’ models are easy to specify. For further general applicability, it allows the user to define their own fit functions, and optimization may be performed subject to linear and nonlinear equality or boundary constraints.