Inclusion of logistic regression for classification

We're thrilled to announce the release of version 2.1.0 of the asgl package, bringing powerful enhancements to logistic regression models. Below are the key highlights of this release:
Enhanced Logistic Regression Capabilities: Users can now seamlessly address binary classification problems by setting model='logit'. For those requiring more granular control, the options model='logit_raw' and model='logit_proba' are available, providing outputs before logistic transformation and probability outputs, respectively.
Advanced Penalization Options: This update also introduces the implementation of ridge and adaptive ridge penalizations, accessible via penalization='ridge' or penalization='aridge'. These features allow for more flexible model tuning, enhancing adaptability and precision.