The package lassopack implements lasso ( Tibshirani 1996), square-root lasso ( Belloni et al. 2011), elastic net ( Zou & Hastie 2005), ridge regression ( Hoerl & Kennard 1970), adaptive lasso ( Zou 2006) and post-estimation OLS. lassopack also supports logistic lasso.
pdslasso offers methods to facilitate causal inference in structural models. The package allows to select control variables and/or instruments from a large set of variables in a setting where the researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables of interest.
pystacked implements stacking regression ( Wolpert, 1992) via scikit-learn’s sklearn.ensemble.StackingRegressor and sklearn.ensemble.StackingClassifier. Stacking is a way of combining predictions from multiple supervised machine learners (the “base learners”) into a final prediction to improve performance.
I maintain a separate website for these packages: ↪statalasso.github.io/