• basic (dichotomous) ML logistic regression with influence statistics
• fit diagnostics and ROC curve
• Skewed logistic regression
• grouped data logistic regression
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
  

Conditional logistic regression

• McFadden's choice model
• 1:1 and 1:k matching
• conditional fixed-effects logit models (m:k matching) with exact
likelihood (no limit on panel size)
• robust, cluster–robust, bootstrap, and jackknife standard errors
• linear constraints
• Predictions for influence and lack-of-fit statistics and Pearson residuals
  

Multinomial logistic regression

• robust, cluster–robust, bootstrap, and jackknife standard errors
• linear constraints

Probit regression

• dichotomous outcome with ML estimates
• bivariate probit regression
• endogenous regressors
• grouped data probit regression
• heteroskedastic probit regression
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
  

Ordinal regression models

• ordered logistic (proportional odds model)
• ordered probit
• robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
  

Tobit regression and truncated regression

• lower and upper limits of censoring
• differing limits each observation
• predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
• endogenous regressors
• robust, cluster–robust, bootstrap, and jackknife standard errors for truncated regression
• linear constraints

Interval regression

• open and closed intervals
• robust, cluster–robust, bootstrap, and jackknife standard errors for interval regression
• linear constraints

Poisson and negative-binomial regression

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predict expected counts, incidence rates, and probabilities of counts
• Poisson goodness-of-fit tests

• Plackett–Luce model, exploded logit, choice-based conjoint analysis
• Alternative- and case-specific variables
  
• Complete rankings of ordered outcome
• Incomplete rankings of ordered outcome
• Ties (“indifference”)
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Prediction of probability that alternatives are ranked first
  

Stereotype logistic regression

• Predictions of probabilities of outcomes
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints

• Compare estimated means, intercepts, and slopes
  
• Compare marginal means, intercepts, and slopes
  
• Balanced and unbalanced data
  
• Nonlinear responses
  
• Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
  
• Group comparisons that are significant
  
• Graphs of pairwise comparisons

• Automatically create indicators based on categorical variables
  
• Form interactions among discrete and continuous variables
  
• Include polynomial terms
  
• Perform contrasts of categories/levels
  

Nested logit

• Random-utilities maximization model
  
• Full maximum-likelihood estimation
• Up to eight nested levels
• fFacilities to set up the data and display the tree structure
• Linear constraints, including constraints on inclusive value parameters
• Predictions available for utility functions, probabilities, conditional probabilities, and inclusive values
• Robust, cluster–robust, bootstrap, and jackknife standard errors
  

Multinomial probit regression

• alternative- and case-specific variables
• homo- or heteroskedastic variances
• various correlation structures, including user-specified
• probabilities based on GHK simulator
• robust, cluster–robust, bootstrap, and jackknife standard errors
• linear constraints

Heckman selection models

• Two-step and maximum likelihood (ML)
• pRobust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
• Bootstrap and jackknife standard errors (two-step)
• Linear constraints (ML only)
• Predictions available for Mills’ ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more
  

Heckman selection with a binary outcome

• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Predictions available for probability of binary outcome, all four combinations of outcome and selection, probability of selection, conditional probability of outcome, and more

Zero-inflated count models

• zero-inflated Poisson
• zero-inflated negative binomial
• robust, cluster–robust, bootstrap, and jackknife standard errors
• linear constraints
• Predict expected counts, incidence rates, and probabilities of counts
  

Left-truncated count models

• zero-truncated Poisson
• zero-truncated negative binomial
• Left-truncated Poisson*
• Left-truncated negative binomial*
  
• Truncation varying by observation*
• robust, cluster–robust, bootstrap, and jackknife standard errors
• linear constraints
• Predict expected counts, incidence rates, and probabilities of counts
  

Treatment-effects model

• Two-step and maximum likelihood (ML)
  
• Fitted values and their standard errors (SEs)
  
• Expected value given treatment or nontreatment and their SEs
• Probability of treatment and its SE
• Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
• Bootstrap and jackknife standard errors (two-step)
  
• Linear constraints (ML only)

Marginal analysis

• Estimated marginal means
• Marginal and partial effects
  
• Average marginal and partial effects
  
• Least-squares means
  
• Predictive margins
  
• Adjusted predictions, means, and effects
  
• Contrasts of margins*
• Pairwise comparisons of margins*
• Profile plots*
• Graphs of margins and marginal effects*
  

• Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects
  
• Comparisons against reference groups, of adjacent levels, or against the grand mean
  
• Orthogonal polynomials
  
• Helmert contrasts
  
• Custom contrasts
  
• ANOVA-style tests
  
• Contrasts of nonlinear responses
  
• Multiple-comparison adjustments
  
• Balanced and unbalanced data
  
• Contrasts in odds-ratio metric
  
• Contrasts of means, intercepts, and slopes
  
• Graphs of contrasts
  
• Interaction plots