OpenMx Help

OpenMx Does Not Provide Correct Results for Example Code?
I'm using the latest version of OpenMx on Ubuntu 16.04. I re-installed it a few minutes ago hoping it would fix what appears to be wrong. I'm also running OpenMx on Ubuntu 15.04 on another computer which appears to work correctly.
Here is the example code from mxTryHard:
library(OpenMx)
data(demoOneFactor) # load the demoOneFactor dataframe

OpenMx installation in Linux CentOS 6.8
I am not able to compile openmx under 3.3.0. I was advised to change the compiler by including
CXX=g++ in the ~/.R/Makevars file.
But the openMx is still being compiled using icpc instead of g++ and I was asked by the help desk to contact the developers.
Any help?
Thanks
Anbu.
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R install of openmx breaks with R update 3.3.2
I had the R version of openmx 2.3.1 working on R 3.2 but after the update it no longer will install or update. I was trying to go to 2.6.9, but now not even 2.3.1 will install.
OS RHEL 6
error message attached:

Using GCC 4.9
I'm trying to install OpenMx, but it will not compile. For reasons that are complex and that I don't fully understand, I cannot use GCC beyond version 4.9, and having investigated on this forum, I suspect that this is the reason for the failure.
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OpenMx Fails to install in Ubuntu 16.04
I'm running Ubuntu 16.04. I get this message:
WARNING: Failed to download libnpsol.a from http://openmx.psyc.virginia.edu/packages/npsol/linux/x86_64/gcc5.4/libnpsol.a
** libs
Here are the details:
rick@rick-CT14:~$ sudo R
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Optimization issues - binary with low prevalence
Hey,
I work with OpenMx using a bit different data than most others; often data comes from a full population and has quite many rows (up to 3 million). A common type of analysis is for relatives with one or more binary variables, e.g. observed disease diagnosis, where the prevalence is low, e.g. 1% to 0.05%. The complexity of the models vary from simple 2x2 covariance matrices without any definition variables to 8x8 covariance matrices with several definition variables adjusting the means/thresholds.

Power estimation for the detection of rG and rE
How can one estimate the power for the detection of significant rG and rE in multivariate Cholesky models?
Specifically, the analysis employed a trivariate Cholesky (AE providing the best fit, with all significant rGs, and one significant rE). The CIs have been calculated for all estimates. The sample is on the small side: 200 same-sex pairs (half MZ, half DZ) and may have been underpowered for the other smaller rEs but I’m not sure what size effect I had enough power to detect.

Error Meesage from mxGenerateData from Documentation Example Code
I included "facLoads" in the mxModel arguments so I could get estimates of the factor loadings. (It was missing from code in the on-line documentation.)
When I try to use mxGenerateData, it gives an error.
http://openmx.psyc.virginia.edu/docs/OpenMx/2.6.7/_static/demo/OneFactorJoint_PathRaw.R
#
# Copyright 2007-2012 The OpenMx Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#

How to install openMX
Hello,
I am new to OpenMX.I tried to install openmx ,but i get this error message on my r-terminal. How to install openmx in my windows 10 system? I want to be able to know how much memory have OpenMX.? Please give me more details?
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Error in runHelper NLOPT unrecognized error -1
While running simulations with the development version of OpenMx, I ran into an interesting error:
Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
NLOPT unrecognized error -1; please report to developers
It is somewhat difficult to replicate and it took some time to put together the attached example. As you may see in the attached, I read in the same dataset and fit the same model 10 times, obtaining likelihood-based CIs each time for a single parameter. The error only shows up for some replications.
Thought you all should know!
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