Version 2.3.1 introduces several new features:
mxRun()now displays the number of free parameters in the MxModel before calling the compiled "backend."
mxFactorScores()is now compatible with RAM models and multigroup models.
coef()is now defined for MxModels, as a wrapper to
mxCheckIdentification()is now compatible with GREML expectation.
Version 2.3.1 also includes a number of bug-fixes and performance tweaks:
mxTryHard()have been added.
mxGetExpected()'s compatibility with LISREL models has been improved.
mxFitFunctionGREML(), and its argument
mxComputeGradientDescent()to provide the optimizer with the upper-triangular Cholesky factor of the Hessian matrix at the start values, which can cut down the number of function evaluations the optimizer needs to minimize the fitfunction.
Finally, one known issue with version 2.3.1 is that the factor-score estimates returned by
mxFactorScores(), when using
'WeightedML', are deviations from the latent variable's mean. If the latent variable does not have a mean of zero, then the score estimates must be shifted appropriately in order to be correct.