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) <doi:10.48550/arXiv.1603.01700>.

Version:	0.3.2
Depends:	R (≥ 3.0.0)
Imports:	MASS, glmnet, ggplot2, checkmate, Formula, methods
Suggests:	testthat, knitr, rmarkdown, formatR, xtable, mvtnorm, markdown
Published:	2024-02-14
DOI:	10.32614/CRAN.package.hdm
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
Materials:	README
In views:	CausalInference, Econometrics, MachineLearning
CRAN checks:	hdm results

Documentation:

Reference manual:	hdm.pdf
Vignettes:	High-Dimensional Metrics in R

Downloads:

Package source:	hdm_0.3.2.tar.gz
Windows binaries:	r-devel: hdm_0.3.2.zip, r-release: hdm_0.3.2.zip, r-oldrel: hdm_0.3.2.zip
macOS binaries:	r-release (arm64): hdm_0.3.2.tgz, r-oldrel (arm64): hdm_0.3.2.tgz, r-release (x86_64): hdm_0.3.2.tgz, r-oldrel (x86_64): hdm_0.3.2.tgz
Old sources:	hdm archive

Reverse dependencies:

Reverse depends:	tsapp
Reverse imports:	ablasso, causalweight, hdcate
Reverse suggests:	DirectEffects

Linking:

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