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hdm (version 0.3.1)
High-Dimensional Metrics
Description
Implementation of selected high-dimensional statistical and
econometric methods for estimation and inference. Efficient estimators and
uniformly valid confidence intervals for various low-dimensional causal/
structural parameters are provided which appear in high-dimensional
approximately sparse models. Including functions for fitting heteroscedastic
robust Lasso regressions with non-Gaussian errors and for instrumental variable
(IV) and treatment effect estimation in a high-dimensional setting. Moreover,
the methods enable valid post-selection inference and rely on a theoretically
grounded, data-driven choice of the penalty.
Chernozhukov, Hansen, Spindler (2016) .