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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).
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
The new features are described in Mplus Web Talk No. 2: Using Mplus to do Latent Transition Analysis and Random Intercept Latent Transition Analysis and the technical appendix LTA Interpretation of Probabilities, Odds, and Odds Ratios Results
Expanded Bayesian fit statistics in 3 areas: improved posterior predictive p-values (PPP) when there are missing data, Bayesian CFI/TLI/RMSEA including confidence intervals, and a Bayesian version of the Wald test of parameter restrictions using the MODEL TEST command.
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