We are pleased to announce the official release of OpenMx version 2.18.1. Click here for instructions on how to install the package from our repository. As usual, our repository has package binaries for Windows and macOS, and source tarballs for Linux/GNU and other non-Mac Unix-likes, all of which come with the proprietary NPSOL optimizer. Alternately, users may install the fully open-source build of the new version from CRAN.
As most of you know, R version 4.0 was released in April of this year. OpenMx 2.18.1 marks the first release for which our NPSOL-enabled package builds will be exclusively compatible with R version 4. CRAN, however, will likely continue building OpenMx for R 3.6 until R 4.1 is released. Mac users should further note that our NPSOL-enabled Mac build of OpenMx 2.18.1 is only compatible with macOS 10.14 or newer.
The most significant change in this new OpenMx release is a change in the on-load default options. Specifically, SLSQP, not CSOLNP, is now the on-load default optimizer. We made this change because we encountered multiple instances where CSOLNP performed very slowly when there were MxConstraints in the MxModel.
omxSetParameters()
now defaults to affecting all free parameters in the MxModel. Note that this is a backwards-incompatible change in default behavior.
mxRun()
now throws an error. This new behavior is intended to prevent users from carelessly hogging computational resources on shared computing clusters.
predict.MxModel
now exists, but is implemented only for state-space models.
mxTryHard*()
that caused it to return WLS models with incorrect chi-square statistics. This serious bug has been repaired.
mxTryHard*()
to spuriously flag all fit attempts as failures if the model contained MxConstraints that were not satisfied at the start values. This bug has been repaired.
omxReadGRMBin()
that made it practically unusable with argument returnList=FALSE
. This bug has been repaired.
omxGetNPSOL()
now gives directions to Windows users that will work as intended under RStudio.
free=TRUE
, and throws an error.
mxGenerateData()
now behaves as designed with argument use.miss=TRUE
and non-missing argument nrowsProportion
.
mxCheckIdentification()
, mxEval()
, mxSE()
, and finally, mxRun()
when analyzing ordinal data.
mxCompare()
does not work correctly with WLS models. This bug is expected to be repaired in the next OpenMx release.