hdm: 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.

Version: 0.2.0
Depends: R (≥ 3.0.0)
Imports: MASS, glmnet, ggplot2, checkmate, Formula, methods
Suggests: testthat, knitr, xtable
Published: 2016-06-17
Author: Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut]
Maintainer: Martin Spindler <spindler at mea.mpisoc.mpg.de>
License: GPL-3
NeedsCompilation: no
Citation: hdm citation info
In views: MachineLearning
CRAN checks: hdm results

Downloads:

Reference manual: hdm.pdf
Vignettes: High-Dimensional Metrics, lasso
Package source: hdm_0.2.0.tar.gz
Windows binaries: r-devel: hdm_0.2.0.zip, r-release: hdm_0.2.0.zip, r-oldrel: hdm_0.2.0.zip
OS X Mavericks binaries: r-release: hdm_0.2.0.tgz, r-oldrel: hdm_0.2.0.tgz
Old sources: hdm archive

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