I have used GLMnet with LASSO quite a bit for classification in R. It’s just like other GLM-like linear models; you give it input variables, a boolean output variable, and it returns a model. Actually it gives you a range of models that vary by lambda, its hyperparameter - different lambdas give you different coefficient mixes. So, you can then use CV or similar methods to get the best-performing lambda. GLMnet also works well for cox and regression models. Elasticnet is related, with a better penalty mechanism.
But this may not be what you were asking. I can’t tell. What did you try and what didn’t work?