Not positive definite error when all within-studies covariance matrices are positive definite

Hi Mike (and the rest of the forum),

Thanks so much for maintaining your metaSEM package!

I have been trying to fit a variation of a network meta-analysis model using your package. In particular, I need to impose specific constraints on the estimated between-studies covariance matrix. However, my question is not about the constraints that I'm imposing, but rather about a not positive definite error that I haven't been able to figure out.

If you read in the attached data and use the check_pd() function to test whether the within-studies covariance matrices are positive definite, you will see that they all are. However, when I attempt to run my model using meta(), I get the following error:

"The job for model 'Meta analysis with ML' exited abnormally with the error message: MxComputeGradientDescent: fitfunction Meta analysis with ML.fitfunction is not finite (Expected covariance matrix for continuous variables is not positive-definite in data row 32)"

What's odd is that I've fit a similar model using the mvmeta package (without the constraints on the between-studies covariance matrix that I want -- this isn't possible in mvmeta) without any errors. So, I'm forced to conclude that either I've mis-specified my model using meta() or that something strange is occurring within meta() or mxRun().

Do you have any suggestions for what might be happening?