Package: hdm
Type: Package
Title: High-Dimensional Metrics
Version: 0.3.1
Date: 2018-12-19
Authors@R: c(person("Martin", "Spindler", email="martin.spindler@gmx.de", role=c("cre", "aut")), person("Victor", "Chernozhukov", role="aut"), person("Christian", "Hansen", role="aut"),  person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role="ctb"))
Depends: R (>= 3.0.0)
Description: 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>.
License: MIT + file LICENSE
LazyData: TRUE
Imports: MASS, glmnet, ggplot2, checkmate, Formula, methods
Suggests: testthat, knitr, xtable, mvtnorm
VignetteBuilder: knitr
RoxygenNote: 6.1.0
Author: Martin Spindler [cre, aut],
  Victor Chernozhukov [aut],
  Christian Hansen [aut],
  Philipp Bach [ctb]
Maintainer: Martin Spindler <martin.spindler@gmx.de>
Repository: CRAN
Repository/R-Forge/Project: hdm
Repository/R-Forge/Revision: 160
Repository/R-Forge/DateTimeStamp: 2019-01-18 15:08:29
Date/Publication: 2019-01-18 21:50:17 UTC
NeedsCompilation: no
Packaged: 2019-01-18 15:34:25 UTC; rforge
Built: R 3.4.4; ; 2019-04-25 21:40:48 UTC; windows
