As part of a statistical learning research paper I am collaborating on, I am running/fitting two hundred sixty thousand different LASSO Regressions on the same number of different randomly generated synthetic data sets and calculating some standard classification performance measurements (True Positive Rate, True Negative Rate, and False Positive Rate) so that I can use these measurements as Benchmark performance metrics to compare the performance of the novel supervised statistical learning algorithm for variable selection eating evaluated in the paper/study on the same 260K data sets with.
I am going to be using the statistical programming language are for this purpose because that is the programming language suitable for this task which I am most comfortable with by far. What would be the best function and corresponding package to use for this task?
I will accept any suggestion BESIDES the enet function from the elasticnet package because I have had issues working with this function in the past.
p.s. I understand that in order to get it to work on the 260K data sets sequentially, whatever function it turns out to be best suited, it will need to be implemented within an lapply or a parLapply function.