Fitting possibly high dimensional penalized
        regression models. The penalty structure can be any combination
        of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
        positivity constraint on the regression coefficients. The
        supported regression models are linear, logistic and Poisson
        regression and the Cox Proportional Hazards model.
        Cross-validation routines allow optimization of the tuning
        parameters.

Utilize the CDF penalty function to estimate a penalized linear model. 
  It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. 
  For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.

Implements L1 and L2 penalized conditional logistic regression with penalty 
    factors allowing for integration of multiple data sources. Implements
    stability selection for variable selection.

Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
        functions is implemented. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
        parameters can be found using either a fixed grid or a interval
        search.

Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.

Computes penalized regression calibration (PRC), a
    statistical method for the dynamic prediction of survival when many
    longitudinal predictors are available. PRC is described in Signorelli
    (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021)
    <doi:10.1002/sim.9178>.

Estimating Non-Simplified Vine Copulas Using Penalized Splines.