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: ${ \lambda}$ for the complexity and ${ \alpha}$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting ${ \lambda}$ for a given ${ \alpha}.$ However, glmnet does not simultaneously search the ${ \lambda} - { \alpha}$ 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 ${ \lambda} - { \alpha}$ space and identify an optimal ${ \lambda} - { \alpha}$ pair. Availability: ensr is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=ensr
Categories: stat.CO