Return a regularized fit to a linear regression model.
Parameters:  method : :
maxiter : integer
alpha : scalar or arraylike
L1_wt : scalar
start_params : arraylike
cnvrg_tol : scalar
zero_tol : scalar


Returns:  A PHregResults object, of the same type returned by `fit`. : 
Notes
The penalty is the”elastic net” penalty, which is a convex combination of L1 and L2 penalties.
The function that is minimized is: ..math:
loglike/n + alpha*((1L1_wt)*params_2^2/2 + L1_wt*params_1)
where and are the L1 and L2 norms.
Postestimation results are based on the same data used to select variables, hence may be subject to overfitting biases.