
umx 2.6.0 
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### May 2016 R 3.3.0 "Supposedly Educational" release

#### New features include:
* mxFitFunctionML gains an option to parallel-ize gradient- rather than row-computation.
 * Can decrease run-times by 50% for some models!

#### Bug fixes and performance tweaks include:
* 


umx 2.5.2
--------------

New features include:
* Regression factor-score estimates are now available for RAM path models via mxFactorScores().
* mxGenerateData() can now generate data conditional on definition variables.
* SLSQP is now capable of using an analytic gradient during optimization.
* Substantial improvements in mxTryHard() and new specialist helpers:
  * mxTryHardOrig()
  * mxTryHardSSCT()
  * mxTryHardWideSearch()
  * mxTryHardOrdinal()
* A new function, imxRobustSE(), which calculates robust standard errors for parameter estimates, from the "sandwich estimator."
* Some functions have been newly made usable in MxAlgebras: the inverse trigonometric functions, the inverse hyperbolic functions, logp2z() (standard-normal quantile function from log probabilities), lgamma1p() (accurate lgamma(x+1) for small x), the Bessel functions, dbeta(), and pbeta(). The latter two are prototypes for making the 'd' and 'p' probability-distribution functions from the 'stats' package usable in MxAlgebras.

Bug fixes and performance tweaks include:
* Two GREML-related bugs have been repaired.
* LISREL path models now handle means correctly.
* mxFactorScores() now returns factor scores in original (not sorted) order.
* Factor-score estimates on factors with nonzero means now work.
* mxFactorScores() with type="ML" or type="WeightedML" now works
* Several help pages have been updated, clarified, and made more complete.
* NPSOL consistently respects the value of the "Major iterations" option.
* Newton-Raphson optimizer performs better at parameter bounds.
* mxGenerateData() works with continuous-time state-space models.


umx 2.3.1
--------------

Version 2.3.1 includes a number of new features
* mxRun() now displays the number of free parameters in the MxModel
* Multigroup WLS has been implemented.
* OpenMx now checks whether the Hessian matrix is convex at the solution
* mxFactorScores() is now compatible with RAM models and multigroup models.
* coef() is now defined for MxModels, as a wrapper to omxGetParameters().
* mxCheckIdentification() is now compatible with GREML expectation.

Version 2.3.1 also includes a number of bug-fixes and performance tweaks:
* Improvements to mxTryHard().
* Fixes for state-space models.
* mxGetExpected() more compatible with LISREL.
* Better handling of start values in SLSQP.
* Several GREML bugs fixed. In particular, it is now safe to use mxFitFunctionGREML(), and its argument dV.
* Matrix operations in the GREML backend have been streamlined.
* Evaluation of GREML analytic derivatives can now be parallelized.
* Noticeable speed-ups for very large matrices.
* The NPSOL "warm start" now works correctly.

umx 2.2.6
--------------

* Available on CRAN.
* New optimizer: SLSQP
* New data and model types: WLS, LISREL, GREML
* Additional helpers:
* mxMI() Modification indicies for any single-group likelihood-based model.
* mxGenerateData() Generate data based on a single-group RAM, LISREL, Normal, or State Space model.
* mxGetExpected() Get the expected means, covariance, or thresholds from a RAM, LISREL, or Normal model.
* mxCheckIdentification() Check the identification of single or multigroup, RAM, LISREL, Normal, or State Space model.
* omxRMSEA() Get the RMSEA with confidence interval and test of approximate fit. Also, reported in mxSummary.
* mxKalmanScores() Get the Kalman latent state scores from a state space model. It can also be adapted for non-ordinal factor scores.
* mxFactorScores() Estimate latent variable scores from a LISREL factor model via several methods including maximum likelihood and for ordinal data.
* mxAlgebraFromString() Construct an mxAlgebra from an R expression provided as a character string.
* mxFitFunctionMultigroup() used to require "modelname.fitfunction", : now "modelname" will work too.
* Definition variables work correctly with mxFitFunctionRow().
* Confidence intervals are improved:
* parameter vector at each confidence limit ("bound") is now available in mxSummary(verbose=T).
* Improvements to CSOLNP. Note that CSOLNP should still be used with caution
* mxExpectationStateSpaceContinuousTime()
* Improvements to mxTryHard().
* Improvements to the internal interface with NPSOL:
* Further improvements to WLS are expected in subsequent releases.
