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: | 0.2.3 |
Depends: | R (≥ 3.0.0) |
Imports: | MASS, glmnet, ggplot2, checkmate, Formula, methods |
Suggests: | testthat, knitr, xtable |
Published: | 2018-01-23 |
Author: | Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut] |
Maintainer: | Martin Spindler <martin.spindler at gmx.de> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | hdm citation info |
In views: | MachineLearning |
CRAN checks: | hdm results |
Reference manual: | hdm.pdf |
Vignettes: |
High-Dimensional Metrics, lasso |
Package source: | hdm_0.2.3.tar.gz |
Windows binaries: | r-devel: hdm_0.2.3.zip, r-release: hdm_0.2.3.zip, r-oldrel: hdm_0.2.3.zip |
OS X El Capitan binaries: | r-release: hdm_0.2.3.tgz |
OS X Mavericks binaries: | r-oldrel: hdm_0.2.0.tgz |
Old sources: | hdm archive |
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