Longitudinal SEM and Latent Growth Curves

Individual slope and intercept estimates?
My inclination is to get factor score estimates for the intercept and slope factors, but when I do this using mxFactorScores(), I get an error message:
"Error: In model 'FactorScoresLGCM_N' the name 'fscore' is used as a free parameter in 'FactorScoresLGCM_N.Score' but as a fixed parameter in 'FactorScoresLGCM_N.Score'.
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Structured latent growth curve with definition variables
Error in value[rows[[i]], cols[[i]]] <- startValue :
incorrect number of subscripts on matrix

LCS: Effect of a time-independent predictor on a covariance

How to use the EM algorithm with a nonlinear latent growth curve model?
$$y_{obs} = \frac{diff}{1+e^{\frac{\beta - time_i}{\gamma}}}, $$

RI-CLPM: covariates and scaling
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Cross-lagged model issues
I have a cross-lagged model that I have run using the code below:
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negative variance in factor representing change in anxiety (latent factor)
I'm having trouble with a model investigating change in anxiety over time. In the future, I will be building out a more complex model with other factors predicting change in anxiety over time, but I'm now just at a step before that looking into a model with only change in anxiety over time--only for two time points.

How to implement FIML estimation in OpenMx?
I am running this code to conduct LGCM prior to GMM.
I was wondering if there is a way of implementing FIML instead of ML in my code.
This is because my data has a lot of missingness by design (longitudinal data) and is not normally distributed.
Many thanks!!!
require(OpenMx)
?MxFitFunction
fitFunction <- mxFitFunctionML(rowDiagnostics=TRUE)
mxOption(NULL,"Default optimizer","SLSQP") ### to avoid msg error: https://openmx.ssri.psu.edu/node/4238
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Help in understanding notation for NO-GROWTH MODEL (OpenMx) and ERROR message
I´m having a hard time understanding the OpenMx notation from this handbook "Growth Modeling" (Grimm, Ram & Estabrook, 2017).
The authors compare the script from MPlus and OpenMx with regard to the specification of a no-growth model (attached as pics). While in Mplus is pretty straightforward, I don´t understand why the latent factor variance (psi_11) and the indicators residual variances are set at 80 and 60 respectively in the OpenMx script.

More questions about the magic of LGM with use of age definition variables
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