SODA: Main and Interaction Effects Selection for Logistic Regression, Quadratic Discriminant and General Index Models

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.


Reference manual

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1.2 by Yang Li, 3 years ago

Browse source code at

Authors: Yang Li , Jun S. Liu

Documentation:   PDF Manual  

GPL-2 license

Depends on nnet, MASS, mvtnorm

See at CRAN