Cross validation informed Relaxed LASSO, Artificial Neural Network (ANN), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Recursive Partitioning ('RPART') or step wise regression models are fit.  Nested cross validation to estimate and compare performances between these models is also performed.  
    For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet().  This may be remedied with the 'path=TRUE' options when calling cv.glmnet().  This option is not described in the 'glmnet' Reference Manual but is described in the 'glmnet' "The Relaxed Lasso" vignette.  In this package, 'glmnetr', we provide a similar workaround and solve for the non penalized relaxed model where gamma=0 for model structures analogue to 'R' functions like glm() or coxph() of the 'survival' package.  If you are not fitting relaxed lasso models, or if you are able to get convergence using 'glmnet', then the glmnetr() and cv.glmnetr() functions may not be of much benefit to you.  Note, while this package may allow one to fit relaxed lasso models that have difficulties converging using 'glmnet', and provides some different functionality beyond that of cv.glmnet(), it does not afford the some of the versatility of 'glmnet'. 
    When fitting not a relaxed lasso model but an elastic-net model, then the R-packages 'nestedcv' <https://cran.r-project.org/package=nestedcv>, 'glmnetSE' <https://cran.r-project.org/package=glmnetSE> or others may provide greater functionality when performing a nested CV.
    As with the 'glmnet' package, this package passes most relevant output to the output object and tabular and graphical summaries can be generated using the summary and plot functions.  Use of the 'glmnetr' has many similarities to the 'glmnet' package and it is recommended that the user of 'glmnetr' first become familiar with the 'glmnet' package <https://cran.r-project.org/package=glmnet>, with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially helpful in this regard.