I'm pleased to announce that the glmnetUtils package is now available on CRAN. 
I wrote this after using the popular glmnet package to fit elastic net models 
for a few customer projects, and rewriting the same boilerplate code each time.

glmnetUtils provides some quality-of-life improvements for glmnet, specifically:

- a formula/data frame interface to glmnet and cv.glmnet. No more manual 
fiddling around with model.matrix/as.matrix/data.matrix, worrying about NA 
values, etc.
- a cva.glmnet function to choose both the alpha and lambda parameters via 
cross-validation, following the approach described in the help page for 
cv.glmnet. Optionally does the cross-validation in parallel.
- Methods for predict and coef for the above.

In addition, to facilitate analysis of very wide datasets, glmnetUtils by 
default takes a slightly different approach to generating model matrices. 
Rather than calling model.frame() and model.matrix() on the full dataset, it 
builds up the model.matrix term by term. This is because model.frame() creates 
a terms object, which can be excessively large for wide data. Building up the 
model matrix one term at a time avoids this problem.

CRAN link: https://cran.r-project.org/package=glmnetUtils

Github repo: https://github.com/hong-revo/glmnetUtils

Please feel free to contact me with suggestions, bug reports, criticisms, etc.

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