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.
|Author||Yang Li, Jun S. Liu|
|Date of publication||2017-05-29 21:29:06 UTC|
|Maintainer||Yang Li <[email protected]>|
|Package repository||View on CRAN|
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