Dependent variables

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

Types of models

• Multilevel models
• Hierarchical models
• Mixed models
• Two-, three-, and multiway 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

Residual-error structures for linear models

• Independent
• Exchangeable
• Autoregressive
• Moving-average
• Unstructured

Estimation

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

Other features

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

Predictions

• Predicted outcomes with and without effects
• Predicted effects
• Pearson, deviance, and Anscombe residuals for binary and count outcomes
• Continuous outcomes
• Best linear unbiased predictions (BLUPs) of any or all effects
• BLUPs of fitted values
• Standard errors of BLUPs
• 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
• Predictive margins, marginal means, average marginal effects
• Hausman tests