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Website to be down on Thursday 2/16, 2-4PM EST

The OpenMx website will be taken down for maintenance on Thursday, February 16th, from 2:00 to 4:00 PM, Eastern (US) time.

Website to be down on Friday 1/13, 2-4PM

The OpenMx website will be taken down for maintenance on Friday, January 13th, from 2:00 to 4:00 PM, Eastern (US) time.

OpenMx website to be down for maintenance on Friday, 11/4, and possibly Saturday, 11/5

The server for openmx.psyc.virginia.edu will be down for maintenance on Friday, November 4th, and possibly for Saturday, November 5th, as well.

Version 2.6.7 of OpenMx now available

The newest release of OpenMx, version 2.6.7, is now available through CRAN and through our own repository.

Version 2.5.2 of OpenMx now available

The newest release of OpenMx, version 2.5.2, is now available through CRAN and through our own repository.

New features include:

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  • Regression factor-score estimates are now available for RAM path models via mxFactorScores().
  • Version 2.3.1 of OpenMx now available

    The newest release of OpenMx, version 2.3.1, is now available through CRAN and through our own repository.

    Version 2.3.1 introduces several new features:

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  • When invoked, mxRun() now displays the number of free parameters in the MxModel before calling the compiled "backend."
  • OpenMx now checks whether the Hessian matrix is convex at the solution, and if it is not, throws a warning (status code 5).
  • Advanced Genetic Epidemiology Statistical Workshop: October 26-30 2015, in Richmond, VA

    We are pleased to announce the NIDA and OBSSR funded 'Advanced Genetic Epidemiology Statistical Workshop: Applications to Drug Abuse' was held October 26-30 2015 in Richmond, Virginia.

    New features in OpenMx v2.2

    New Features Since 2.0.1

    OpenMx has a number of new features available in version 2.2.x.

    Perhaps most significantly, we have a new optimizer: SLSQP - An open source BFGS optimizer from the NLOPT collection. Our experience is that SLSQP performs similarly to NPSOL on unconstrained problems and outperforms it on some constrained problems. SLSQP is our new default optimizer.

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