Discrete Choice (DC)
============================
Provides an adaptable, efficient, and user-friendly environment for linear data classification.

Description
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**Discrete Choice Analysis Tools 2.1** provides an adaptable, efficient, and user-friendly environment for linear data classification. It's designed with a full suite of tools built to accommodate individual model specificity, including adjustable parameter bounds, linear or nonlinear constraints, default or user-specified starting values, and user specified Gradient and Hessian procedures. Newly incorporated data and parameter input procedures make model set-up and implementation intuitive.

Installation
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If you're interested in purchasing **DC** Please `contact us <https://www.aptech.com/contact-us>`_ to request pricing and installation information.

If you already own **DC** , you can use the `GAUSS Package Manager <https://www.aptech.com/blog/gauss-package-manager-basics/>`_ for quick download and installation.

Requires GAUSS/GAUSS Engine/GAUSS Light v14 or higher.

Key Features
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Binary and Count Models 
++++++++++++++++++++++++

* Binary probit.
* Binary logit.
* Negative binomial regression.
* Poisson regression.

Multinomial Logit Models 
+++++++++++++++++++++++++

* Conditional logit.
* Nested logit.
* Ordered logit.
* Adjacent category logit.
* Stereotype logit.

Logistic Regression Modelling 
++++++++++++++++++++++++++++++

* L2/L1 regularized classifiers.
* L2/L1-loss linear SVM.

Accessible, Storable, and Exportable Output 
++++++++++++++++++++++++++++++++++++++++++++

* Parameter estimates.
* Variance-covariance matrix for coefficient estimates and marginal effects.
* Categorical dependent variables percentages.
* Data descriptions of all independent variables.
* Marginal effects of independent variables.
* Predicted counts and residuals.

Model Selection and Assessment 
+++++++++++++++++++++++++++++++

* Full model and restricted model log-likelihoods.
* Chi-square statistic.
* Agresti’s G-squared statistic.
* McFadden’s Pseudo R-squared.
* Madalla’s Pseudo R-squared.
* Akaike information criterion (AIC).
* Bayesian information criterion (BIC).
* Likelihood ratio statistics and accompanying probability values.
* McKelvey and Zovcina’s psuedo R-Squared.
* Cragg and Uhler’s normed likelihood ratios.
* Count R-Squared.
* Adjusted count R-Squared.