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.
Version: |
0.2.0 |
Depends: |
R (≥ 3.0.0) |
Imports: |
MASS, glmnet, ggplot2, checkmate, Formula, methods |
Suggests: |
testthat, knitr, xtable |
Published: |
2016-06-17 |
Author: |
Martin Spindler [cre, aut],
Victor Chernozhukov [aut],
Christian Hansen [aut] |
Maintainer: |
Martin Spindler <spindler at mea.mpisoc.mpg.de> |
BugReports: |
NA |
License: |
GPL-3 |
URL: |
NA |
NeedsCompilation: |
no |
Citation: |
hdm citation info |
In views: |
MachineLearning |
CRAN checks: |
hdm results |