High-Dimensional Metrics R package hdm version 0.3.2 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) CRAN Task View: Econometrics (https://CRAN.R-project.org/view=Econometrics) CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) 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>. Software Depends: R (>= 3.0.0) Imports: MASS, glmnet, ggplot2, checkmate, Formula, methods Suggests: testthat, knitr, rmarkdown, formatR, xtable, mvtnorm, markdown Martin Spindler <martin.spindler@gmx.de> Comprehensive R Archive Network (CRAN) Martin Spindler [cre, aut], Victor Chernozhukov [aut], Christian Hansen [aut], Philipp Bach [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=hdm/LICENSE) 2024-02-14 application/tgz https://CRAN.R-project.org/package=hdm doi:10.32614/CRAN.package.hdm