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XWITH for Bayes. This avoids slow computations using ML when many XWITH terms are included. ML bootstrapping to get non-symmetric confidence intervals is not needed with Bayes because such intervals are part of its estimation.
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
Multiple processor computation for Bayes estimation with logit link for binary variables
Saving of data sets and parameter estimates generated using the BOOTSTRAP option of the ANALYSIS command.
Automatic reordering of latent classes for mixture models
Bayes estimation for count, nominal, and binary variables using a logit link for both real data analysis and Monte Carlo simulation studies for single-level, two-level, and mixture models with a single categorical latent variable.
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