Fits the LASSO regression using a customized procedure, with cross-validation
based on origami

cv_lasso_early_stopping(x_basis, y, n_lambda = 100, n_folds = 10)

## Arguments

x_basis |
A `dgCMatrix` object corresponding to a sparse matrix of
the basis functions generated for the HAL algorithm. |

y |
A `numeric` vector of the observed outcome variable values. |

n_lambda |
A `numeric` scalar indicating the number of values of
the L1 regularization parameter (lambda) to be obtained from fitting the
LASSO to the full data. Cross-validation is used to select an optimal lambda
(that minimizes the risk) from among these. |

n_folds |
A `numeric` scalar for the number of folds to be used in
the cross-validation procedure to select an optimal value of lambda. |