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: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=hdm to link to this page.
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