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.

Documentation

Manual: hdm.pdf
Vignette: High-Dimensional Metrics, lasso

Maintainer: Martin Spindler <spindler at mea.mpisoc.mpg.de>

Author(s): Martin Spindler*, Victor Chernozhukov*, Christian Hansen*

Install package and any missing dependencies by running this line in your R console:

install.packages("hdm")

Depends R (>= 3.0.0)
Imports MASS, glmnet, ggplot2, checkmate, Formula, methods
Suggests testthat, knitr, xtable
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Package hdm
Materials
URL
Task Views MachineLearning
Version 0.2.0
Published 2016-06-17
License GPL-3
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NeedsCompilation no
Citation
CRAN checks hdm check results
Package source hdm_0.2.0.tar.gz