Learning Interactions via Hierarchical Group-Lasso Regularization

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) .


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install.packages("glinternet")

1.0.9 by Michael Lim, 4 days ago


http://web.stanford.edu/~hastie/Papers/glinternet_jcgs.pdf


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


Authors: Michael Lim , Trevor Hastie


Documentation:   PDF Manual  


GPL-2 license



Imported by FindIt.


See at CRAN