For my PhD I use a Lasso approach in R for variable selection. Now, I used the package glmnet and also hdm. What is the difference of the basic lasso estimator in these two packages? I read the docs and also googled a lot but the only hint that I found was this one which was not very helpful for my exact purpose.
The reason for asking is because my models converge if I use glmnet and they sometimes do not converge when I use hdm. That is why I assume that the difference is in the optimization function.
I would like to attach a toy example but as the data is private this is rather difficult. And in the end I am more interested in the theory of both packages and maybe I find a good reason to stick to the glmnet package.
Thank you so much in advance!