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Significant speed improvements for Bayesian computations using a new parallelized computing approach (Asparouhov & Muthén, 2019a). In addition, Bayesian algorithms have been reorganized to produce faster computations.
Item Response Theory, and structural equation models for continuous, binary, ordered categorical (ordinal), or combinations of these variable types. The estimation is carried out using Bayesian analysis
Counterfactually-defined causal effects for ordinal mediators and ordinal outcomes
New plots: Bootstrap distributions, moderation plots, and sensitivity plots (TYPE=SENSITIVITY PLOT2;)
Saving of propensity scores
Test of proportional odds for logistic regression with ordinal outcomes (Brant Wald Test)
Random starts for weighted least squares estimators
Option to generate an ID variable that corresponds to the record number (DEFINE: idvariable = _RECNUM;)
Mplus R functions were updated to handle new plots in Version 7.4
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