Mplus Base Program

Regression, path analysis, factor analysis, SEM, growth modeling using ML-EM
ML-EM using numerical integration
ML missing data under MAR
WLMSV missing data under covariate-MAR
WLSMV chi-square difference testing
WLSMV modification indices

New types of outcomes

Counts - Poisson and zero-inflated Poisson modeling
Censored - Censored-normal and censored-inflated normal
    Regression, path analysis, factor analysis, SEM, growth modeling using ML-EM
    ML-EM using numerical integration
    ML missing data under MAR

Random Slopes

Categorical outcomes
Slopes for exogenous observed variables
Slopes for endogenous observed variables
Slopes for continuous latent variables

Interactions between continuous latent variables and between continuous latent variables and observed variables using ML

Continuous factor indicators
Categorical factor indicators

Non-linear factor analysis

Indirect effects

Bootstrap standard errors and confidence intervals

Nonlinear constraints

Growth Modeling

New language

Categorical outcomes using ML-EM via numerical integration

Two-part growth modeling

Interaction modeling, for example, between initial status and a time-invariant covariate

Automatic starting values for growth factor means and variances for continuous outcomes