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Bayes estimation for the TYPE=IMPUTATION option of the DATA command. This gives non-symmetric CIs with imputed data. The new feature is described in the technical appendix Analyzing Imputed Data with the Bayesian Estimator in Mplus
Speed improvement for Bayes estimation with count variables
The measurements are typically closely spaced in time. In Mplus, both N=1 models and a variety of two-level and cross-classified time series models can be estimated. These include univariate autoregressive, regression, cross-lagged, confirmatory factor analysis,
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 Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
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