Supported Models: Encompasses a large variety of linear classification models

• Large Scale Data Classification:
  Performs large-scale binary linear classification using support vector machines [SVM] or logistic regression [LR] methodology. Available options include cross-validation of model parameters and prediction plotting. Easy to access output includes estimated prediction weights, predicted classifications and cross-validation accuracy.
• Adjacent Categories Multinomial Logit Model: 
  The log-odds of one category versus the next higher category is linear in the cutpoints and explanatory variables.
• Binary Logit and Probit Regression Models:
  Estimates dichotomous dependent variable with either Normal or extreme value distributions.
• Conditional Logit Models: 
  Includes both variables that are attributes of the responses as well as, optionally, exogenous variables that are properties of cases.
• Multinomial Logit Model: 
  Qualitative responses are each modeled with a separate set of regression coefficients.
• Negative Binomial Regression Model (left or right truncated, left or right censored, or zero-inflated): 
  Estimates model with negative binomial distributed dependent variable. This includes censored models - the dependent variable is not observed but independent variables are available - and truncated models where not even the independent variables are observed. Also, a zero-inflated negative binomial model can be estimated where the probability of the zero category is a mixture of a negative binomial consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
• Nested Logit Regression Model: 
  Derived from the assumption that residuals have a generalized extreme value distribution and allows for a general pattern of dependence among the responses thus avoiding the IIA problem, i.e., the "independence of irrelevant alternatives."
• Ordered Logit and Probit Regression Models: 
  Estimates model with an ordered qualitative dependent variable with Normal or extreme value distributions.
• Possion Regression Model (left or right truncated, left or right censored, or zero-inflated): 
  Estimates model with Poisson distributed dependent variable. This includes censored models - the dependent variable is not observed but independent variables are available - and truncated models where not even the independent variables are observed. Also, a zero-inflated Poisson model can be estimated where the probability of the zero category is a mixture of a Poisson consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.
• Stereotype Multinomial Logit Model: 
  The coefficients of the regression in each category are linear functions of a reference regression.