sodavis (1.2)

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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.

Maintainer: Yang Li
Author(s): Yang Li, Jun S. Liu

License: GPL-2

Uses: MASS, mvtnorm, nnet

Released about 1 year ago.

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