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.


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install.packages("sodavis")

1.2 by Yang Li, a year ago


Browse source code at https://github.com/cran/sodavis


Authors: Yang Li , Jun S. Liu


Documentation:   PDF Manual  


GPL-2 license


Depends on nnet, MASS, mvtnorm


See at CRAN