  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
