I have a dataset with a lot of missing values and mix of continuous and categorical variables. I want to use something like group lasso to do features selection. Probably the output is binary 0,1 and so grouped lasso logistic regression seems to be the more sensible choice.
My problem is the very large number of missing values. Deleting non complete rows is not an option.
Is there any R implementation that can be used similarly to the lasso and that can handle missing values and categorical variables at the same time?
A possible solution has been proposed here but it does not refer to any R package.