eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Link
http://link.springer.com/content/pdf/10.1186/s12859-019-2778-5.pdf
Reference10 articles.
1. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B. 2005; 67:301–20.
2. Glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models (R Package). http://CRAN.R-project.org/package=glmnet .
3. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33:1–22.
4. Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Info Proc Manage. 2009; 45:427–37.
5. Ernst MD. Permutation methods: a basis for exact inference. Stat Sci. 2004; 19:676–85.
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