High-Dimensional Metrics

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) .


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install.packages("hdm")

0.2.3 by Martin Spindler, 4 months ago


Browse source code at https://github.com/cran/hdm


Authors: Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut]


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning


MIT + file LICENSE license


Imports MASS, glmnet, ggplot2, checkmate, Formula, methods

Suggests testthat, knitr, xtable


See at CRAN