eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

Abstract Background Regularized generalized linear models (GLMs) are popular regression methods in bioinformatics, particularly useful in scenarios with fewer observations than parameters/features or when many of the features are correlated. In both ridge and lasso regularization, feature shrinkage...

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Bibliographic Details
Main Authors: Julián Candia, John S Tsang
Format: Article
Language:English
Published: BMC 2019-04-01
Series:BMC Bioinformatics
Subjects:
Software
R package
Generalized linear models
Regression
Classification
Regularization
Online Access:http://link.springer.com/article/10.1186/s12859-019-2778-5

Internet

http://link.springer.com/article/10.1186/s12859-019-2778-5

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