Mixture Add-On
What's New In Version 3
Mixture Modeling
Improved and simplified analysis
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Random starts
Automatic starting values for thresholds and intercepts
Simplified input
New growth language
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New types of outcomes
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Counts - Poisson and zero-inflated Poisson modeling
Censored - Censored-normal and censored-inflated normal modeling
Nominal - unordered polytomous (multinomial) modeling
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Multiple categorical latent variables
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Loglinear modeling, loglinear latent class modeling
Latent transition analysis, hidden Markov modeling, including mixtures and covariates
Multiple group analysis using known class
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Latent class analysis with random effects
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Conditional dependence
Discrete-time survival mixture frailty analysis
Factor mixture modeling with categorical outcomes
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Prediction of categorical latent variables
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Observed dependent variables predicting latent classes
Factors predicting latent classes
Twin latent class analysis with ACE factors
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Growth mixture modeling with categorical, counts, or censored-normal outcomes and within-class random effect variances
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