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A Section 3 timing table for 5 mixture examples shows the large speed improvements in version 8.4 relative to 8.3.
Substantially simplified output for mixture models with multiple latent class variables
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Lagged categorical outcomes and random slopes for categorical predictors using latent variable decomposition (latent variable centering) for DSEM and other TYPE=TWOLEVEL models using the Bayes estimator (Asparouhov & Muthén, 2018b, (Download scripts))
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Latent variable decomposition (latent variable centering) of predictors with random slopes for DSEM, RDSEM, and other TYPE=TWOLEVEL models using the Bayes estimator (Asparouhov & Muthén, 2018b, (Download scripts)) as shown in Example 9.1 for a random intercept model in the Mplus Version 8 User’s Guide
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RI-CLPM for both binary and ordinal categorical variables is available using a new Bayes algorithm, including access to the residual parameters of the Theta matrix (Asparouhov & Muthen, 2021a).
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