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. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.

Version: 0.3.1
Depends: R (≥ 3.0.0)
Imports: MASS, glmnet, ggplot2, checkmate, Formula, methods
Suggests: testthat, knitr, xtable, mvtnorm
Published: 2019-01-18
Author: Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb]
Maintainer: Martin Spindler <martin.spindler at gmx.de>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: hdm citation info
In views: CausalInference, Econometrics, MachineLearning
CRAN checks: hdm results


Reference manual: hdm.pdf
Vignettes: High-Dimensional Metrics, lasso


Package source: hdm_0.3.1.tar.gz
Windows binaries: r-devel: hdm_0.3.1.zip, r-release: hdm_0.3.1.zip, r-oldrel: hdm_0.3.1.zip
macOS binaries: r-release (arm64): hdm_0.3.1.tgz, r-oldrel (arm64): hdm_0.3.1.tgz, r-release (x86_64): hdm_0.3.1.tgz, r-oldrel (x86_64): hdm_0.3.1.tgz
Old sources: hdm archive

Reverse dependencies:

Reverse depends: tsapp
Reverse imports: causalweight, hdcate


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