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:
Downloads:
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