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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:

Please use the canonical form https://CRAN.R-project.org/package=hdm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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