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) .


Reference manual

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0.3.1 by Martin Spindler, a year ago

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

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

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

MIT + file LICENSE license

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

Suggests testthat, knitr, xtable, mvtnorm

Depended on by causalweight.

See at CRAN