Mplus
Thursday
December 12, 2024
HOME ORDER CONTACT US CUSTOMER LOGIN MPLUS DISCUSSION
Mplus
Mplus at a Glance
General Description
Mplus Programs
Pricing
Version History
System Requirements
Platforms
Mplus Demo Version
Training
Mplus Web Talks
Short Courses
Short Course Videos
and Handouts
Web Training
Mplus YouTube Channel
Documentation
Mplus User's Guide
Mplus Diagrammer
Technical Appendices
Mplus Web Notes
FAQ
User's Guide Examples
Mplus Book
Mplus Book Examples
Mplus Book Errata
Analyses/Research
Mplus Examples
Papers
References
Special Mplus Topics
Bayesian SEM (BSEM)
Complex Survey Data
DSEM – MultiLevel Time Series Analysis
Exploratory SEM (ESEM)
Genetics
IRT
Measurement Invariance
and Alignment
Mediation Analysis
Missing Data
Mixture Modeling
Multilevel Modeling
Randomized Trials
RI-CLPM
RI-LTA
Structural Equation Modeling
Survival Analysis
How-To
Using Mplus via R -
MplusAutomation
Mplus plotting using R
H5 results
Chi-Square Difference
Test for MLM and MLR
Power Calculation
Monte Carlo Utility
Search
 
Mplus Website Updates
Mplus Privacy Policy
VPAT/508 Compliance

Mixture Add-On
What's New In Version 3

Mixture Modeling

  • Improved and simplified analysis
  • Random starts
    Automatic starting values for thresholds and intercepts
    Simplified input
    New growth language

  • New types of outcomes
  • Counts - Poisson and zero-inflated Poisson modeling
    Censored - Censored-normal and censored-inflated normal modeling
    Nominal - unordered polytomous (multinomial) modeling

  • Multiple categorical latent variables
  • Loglinear modeling, loglinear latent class modeling
    Latent transition analysis, hidden Markov modeling, including mixtures and covariates
    Multiple group analysis using known class

  • Latent class analysis with random effects
  • Conditional dependence
    Discrete-time survival mixture frailty analysis
    Factor mixture modeling with categorical outcomes

  • Prediction of categorical latent variables
  • Observed dependent variables predicting latent classes
    Factors predicting latent classes
    Twin latent class analysis with ACE factors

  • Growth mixture modeling with categorical, counts, or censored-normal outcomes and within-class random effect variances