ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters
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Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: λ for the complexity and α for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting λ for a given α. However, glmnet does not simultaneously search the λ - α space for the optional elastic net model. Results: We built the R package ensr, elastic net searcher. enser extends the functionality of glment to search the λ - α space and identify an optimal λ - α pair. Availability: ensr is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=ensr
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