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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 |
Reference manual: | hdm.pdf |
Vignettes: |
High-Dimensional Metrics in R |
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 depends: | tsapp |
Reverse imports: | ablasso, causalweight, hdcate |
Reverse suggests: | DirectEffects |
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