Version 2.6.7 of OpenMx now available
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The newest release of *OpenMx*, version 2.6.7, is now available through CRAN and through our own repository. The most important change for users to be aware of is that, whereas previously OpenMx defaulted to using one less than the number of cores, it now defaults to using 2 threads. This change was made at the insistence of the CRAN Team to reduce their test-server burden.
Make $ <- method for symmetric matrix enforce symmetry (other set cases too)
Currently, $<- on a Symm matrix allows asymetric assignment. That's bad.
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Make Iden subsetting return the data: mxMatrix("Iden", 2, 2)[1,2] # 0 not 1
Solution: convert to full prior to subsetting.
Matrices try to retain type, but may be best just to convert.
Watch with efficiency of storage vs. CPU
Good if this would be trapped at or before run time…
$ accessor support for mxModel manifestVars latentVars
the model$ accessor for manifestVars and for latentVars should return the contents of the eponymous model slots: model@manifestVars & model@latentVars
Add constraint check for mxCheckIdentification
The mxCheckIdentification() function should check to see if there are any MxConstraint objects. It should stop and give up in this case.
Confidence Interval Reference search broken
See this minimal working example.
library(OpenMx)
data(demoOneFactor)
# ===============================
# = Make and run a 1-factor CFA =
# ===============================
latents = c("G") # the latent factor
manifests = names(demoOneFactor) # manifest variables to be modeled
# ====================
# = Make the MxModel =
# ====================
m1 <- mxModel("One Factor", type = "RAM",
manifestVars = manifests, latentVars = latents,
mxPath(from = latents, to = manifests),
mxPath(from = manifests, arrows = 2),
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Version 2.5.2 of OpenMx now available
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The newest release of OpenMx, version 2.5.2, is now available through CRAN and through our own repository.
Test loading saved R workspace images
We should have a test that loads a save workspace image, if we want to ensure that this behavior is backward compatible.
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Robustify mxRefModels
When the Cholesky of the pairwise complete sample covariance fails, try just starting with the variances and means.
http://openmx.psyc.virginia.edu/thread/4079#comment-6338
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implement mxModel(remove = TRUE, type = LISREL)?
Currently returns no-can-do.
If complications with LISREL, removing all exogenous can be solved would be handy and RAM-consistent
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