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
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
- 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
- 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 error (SE)
- 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 effects
- marginal effects and elasticities
- standard errors and confidence intervals
- computation at means or specified covariate values
- computation for any predicted statistic
Linear and nonlinear combinations
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