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.
MASS, glmnet, ggplot2, checkmate, Formula, methods

c(person("Martin", "Spindler", email="martin.spindler@gmx.de", role=c("cre", "aut")), person("Victor", "Chernozhukov", role="aut"), person("Christian", "Hansen", role="aut"))
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>.

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"))
Martin Spindler [cre, aut],
Victor Chernozhukov [aut],
Christian Hansen [aut],
Philipp Bach [ctb]

High-Dimensional Regression with Measurement Error
Penalized regression for generalized linear models for
measurement error problems (aka. errors-in-variables). The package
contains a version of the lasso (L1-penalization) which corrects
for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>).
It also contains an implementation of the Generalized Matrix Uncertainty
Selector, which is a version the (Generalized) Dantzig Selector for the
case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
knitr, rmarkdown, testthat, flare, igraph, tidyverse, zeallot