Solution Paths for Linear and Logistic Regression Models with Categorical Predictors, with SCOPE Penalty

Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.


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2.0.3 by Daniel Grose, 7 months ago

Browse source code at

Authors: Benjamin Stokell [aut] , Daniel Grose [ctb, cre] , Rajen Shah [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, Rdpack

Linking to Rcpp

See at CRAN