Built on top of the glmnet library by Friedman, Hastie, and Tibshirani
(2010), the plasso package follows Knaus (2021) and comes up with two
functions that estimate least squares Lasso and Post-Lasso models. The
plasso() function adds coefficient paths for a Post-Lasso model to the
standard glmnet() output. On top of that cv.plasso() cross-validates
the coefficient paths for both the Lasso and Post-Lasso model and
provides optimal hyperparameter values for the penalty term lambda.
For reporting a bug, simply open an issue on GitHub. For personal contact, you can write an email to michael.knaus@uni-tuebingen.de.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33 (1): 1–22. https://doi.org/10.18637/jss.v033.i01.
Knaus, Michael C. 2021. “A double machine learning approach to estimate the effects of musical practice on student's skills.” Journal of the Royal Statistical Society: Series A,184(1), 282-300. https://doi.org/10.1111/rssa.12623.