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Logistic/logit regression
- Basic (dichotomous) ML logistic regression with influence
statistics
- Fit diagnostics and ROC curve
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Skewed logistic regression
- Grouped-data logistic regression
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
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Bivariate probit regression
- Endogenous regressors
- Grouped-data probit regression
- Heteroskedastic probit regression
Ordinal regression models
- Ordered logistic (proportional-odds model)
- Ordered probit
- Robust, cluster–robust, bootstrap, and jackknife standard errors
Tobit regression and truncated regression
- Lower and upper limits of censoring
- Differing limits for each observation
- Predictions available for expected value, conditional expected value,
censored expected value, and probability of censoring
- Endogenous regressors
- Bootstrap and jackknife standard errors for tobit regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors
for truncated regression
- Linear constraints
Interval and censored-normal regression
- Open and closed intervals
- Robust, cluster–robust, bootstrap, and jackknife standard errors
for interval regression
- Bootstrap and jackknife standard errors
for censored-normal regression
- Linear constraints
Poisson and
negative-binomial regression
- Poisson goodness-of-fit tests
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Rank-ordered logistic regression
- 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 or cluster–robust standard errors
Stereotype logistic regression
- Predictions of probabilities of outcomes
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
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Nested logit
- Random-utilities maximization model
- Full maximum-likelihood estimation
- Up to eight nested levels
- Facilities 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 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 full maximum likelihood
- Predictions available for Mills’ ratio, expected value,
conditional expected value, probability of selection, nonselection
hazard, and more
- Robust, cluster–robust, bootstrap, and jackknife standard errors
(maximum likelihood estimator only)
- Linear constraints
Heckman selection with a binary outcome
- Predictions available for probability of binary outcome, all four
combinations of outcome and selection, probability of selection,
conditional probability of outcome, and more
- Robust, cluster–robust, bootstrap, and jackknife standard errors
(maximum likelihood estimator only)
- Linear constraints
Zero-inflated models
- Zero-inflated Poisson
- Zero-inflated negative binomial
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Zero-truncated models
- Zero-truncated Poisson
- Zero-truncated negative binomial
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Treatment-effects model
- 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
- Linear constraints
Marginal analysis
- Estimated marginal means
- Predictive margins
- Average marginal effects
- Average adjusted predictions
Linear and nonlinear combinations
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