Mixture Distribution and Latent Class Models

Is there a way to add covariates in growth mixture model
I have a script for the 2-class growth mixture model (please see attached). I am wondering if I can add covariate (say x1) as a covariate of my model. I may want to see the effect of x1 on the latent classes only and also of it on both latent classes and the growth factors. As my understanding, in MPlus, the first scenario can be expressed as "c#1 on x1" and the second one can be written as "c#1 on x1" and "int on x1; slp on x1" of each class, but I have zero idea to translate to OpenMx. Any advice would be appreciated.

Can OpenMx fit Factor Mixture Models (FMM)?
Have you used OpenMx to fit Factor Mixture Models (FMM)? I saw that feature in Mplus but I couldn't figure out whether OpenMx can do it or not. Hence the question. Guidance on how to fit FMM using OpenMx would be much appreciated.
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A couple of questions about GMMs
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Carrying out latent profile analysis (LPA) by adapting code examples for a two-factor model and growth mixture models
With the good documentation and code examples for Two-factor Models and mixture models (specifically the code for a growth mixture model), I tried to combine the two. However, when I run it, I get the following error that I'm having trouble debugging:
Error: The reference 'Class1.fitfunction' is unknown in the algebra named 'Latent Profile Model.mixtureObj'

GMM Start Values
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GMM with missing data
I have been working on applying growth mixture model to psychology data with missing data. I know I can use existing software to deal with the missing data. But I want to understand the math behind of it. There is one paper for the Growth mixture model detailing the math titled "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm". I want to know the methodology of the growth mixture model with missing data. Can anyone show me the math or which paper should I refer to?
Thanks a lot.
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GMM with Concomitant Variable
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Nonlinear factors
I am trying to do a factor model with continuous and ordinal data in which the factors are nonlinear, and hence I am approximating their distribution by a mixture of normal distributions. If OpenMx can support it, are there any examples?
Thanks!
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the number of observed statistics in the mixture models
I am fitting a mixture model for a genetic association test that includes sibships of variable sizes, with observed and missing genotypes; the model specification is based on the Acemix2.R script.
I would like to know if it is possible to obtain the correct number of observed statistics without being necessary to specify it manually in the summary() function.

Estimation of class transitions
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