CatReg: 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.
||Rcpp (≥ 1.0.1), Rdpack
||Benjamin Stokell [aut],
Daniel Grose [ctb, cre],
Rajen Shah [ctb]
||Daniel Grose <dan.grose at lancaster.ac.uk>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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