Hierarchical Regularized Regression

Fits hierarchical regularized regression models to incorporate potentially informative external data, Weaver and Lewinger (2019) . Utilizes coordinate descent to efficiently fit regularized regression models both with and without external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). Support for standard R matrices, sparse matrices and big.matrix objects.


Reference manual

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0.1.7 by Garrett Weaver, a year ago


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

Authors: Garrett Weaver [aut, cre] , Juan Pablo Lewinger [ctb, ths]

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, foreach, bigmemory, methods

Suggests knitr, rmarkdown, testthat, Matrix, doParallel

Linking to Rcpp, RcppEigen, BH, bigmemory

System requirements: C++11

See at CRAN