Overview
Discrete Choice Analysis Tools provides a flexible GAUSS environment for estimating, analyzing, and comparing discrete-outcome models.
- Binary and count models: Binary probit, binary logit, negative binomial regression, and Poisson regression.
- Multinomial models: Conditional, nested, ordered, adjacent-category, and stereotype logit.
- Classification: Regularized classifiers and linear support vector machines.
- Flexible model setup: Parameter bounds, linear or nonlinear constraints, starting values, and user-specified gradient and Hessian procedures.
- Comprehensive results: Parameter estimates, marginal effects, predictions, residuals, covariance estimates, and model-selection statistics.
Selected examples
- Adjacent categories logit model
- Binary logit model
- Conditional logit example
- Logistic regression example
- Nested logit model
View Discrete Choice Analysis Tools documentation Contact us about pricing
The documentation includes current capabilities, commands, installation guidance, and examples where available.