glinternet: 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) <DOI:10.1080/10618600.2014.938812>.

AuthorMichael Lim, Trevor Hastie
Date of publication2017-01-01 10:09:09
MaintainerMichael Lim <michael626@gmail.com>
LicenseGPL-2
Version1.0.3
http://web.stanford.edu/~hastie/Papers/glinternet_jcgs.pdf

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Files

glinternet
glinternet/src
glinternet/src/c_routines.c
glinternet/src/Makevars
glinternet/src/fista.c
glinternet/NAMESPACE
glinternet/R
glinternet/R/glinternet.cv.r
glinternet/R/rescale_betahat.r
glinternet/R/onAttach.R
glinternet/R/group_lasso.r
glinternet/R/print.glinternet.cv.r
glinternet/R/glinternet.r
glinternet/R/initialize_betahat.r
glinternet/R/get_lambda_grid.r
glinternet/R/coef.glinternet.cv.r
glinternet/R/get_candidates.r
glinternet/R/extract_effects.r
glinternet/R/predict.glinternet.cv.r
glinternet/R/check_kkt.r
glinternet/R/standardize.r
glinternet/R/mynorm.r
glinternet/R/get_group_sizes.r
glinternet/R/predict.glinternet.r
glinternet/R/c_routines.r
glinternet/R/print.glinternet.r
glinternet/R/strong_rules.r
glinternet/R/plot.glinternet.cv.r
glinternet/R/coef.glinternet.r
glinternet/README.md
glinternet/MD5
glinternet/DESCRIPTION
glinternet/man
glinternet/man/coef.glinternet.Rd glinternet/man/predict.glinternet.cv.Rd glinternet/man/glinternet.Rd glinternet/man/predict.glinternet.Rd glinternet/man/glinternet.cv.Rd glinternet/man/plot.glinternet.cv.Rd

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