• Continuous
• Binary—logistic model
• Count—Poisson model

• Multilevel models
• Hierarchical models
• Mixed models
• Two-, three-, and multi-way random-effects models
• Crossed random effects

Types of effects

• Random effects (variance components)
  • Random intercepts
  • Random coefficients
• Fixed effects

Effect covariance structures

• Identity — shared variance parameter for specified effects with no covariances
• Independent — unique variance parameter for each specified effect with no covariances
• Exchangeable — shared variance parameter and single shared covariance parameter for specified effects
• Unstructured — unique variance parameter for each specified effect and unique covariance parameter for each pair of effects
• Compound — any combination of the above

• Maximum likelihood (ML)
• Restricted maximum likelihood (REML)

Other features

• Factor notation for specifying effects
• Allow unbalanced designs and unbalanced panels
• EM method starting values

Predictions

• Predicted outcomes with and without effects
• Predicted effects
• Pearson, deviance, and anscombe residuals for binary and count outcomes
• Continous outcomes
  • Best linear unbiased predictions (BLUPs) of any or all effects
  • BLUPs of fitted values
  • Residuals and standardized residuals

Postestimation analysis

• Linear and nonlinear combinations of coefficients with SEs and CIs
• Wald tests of linear and nonlinear constraints
• Likelihood-ratio tests
• Linear and nonlinear predictions
• Summarize the composition of nested groups
• Adjusted predictions
• Information criteria — AIC and BIC
• Marginal effects and elasticities with SEs and CIs
• Hausman tests