Mimics the lm function found in the recommended package
stats to fit high dimensional regression models with point
estimates, standard errors, and p-values. Methods for printing
and summarizing the results are given. Future work will
additionally include methods for plotting and predicting.

Mimics the lm function found in the recommended package
stats to fit high dimensional regression models with point
estimates, standard errors, and p-values. Methods for printing
and summarizing the results are given. Supports lasso
estimator, Dantzig selector, mc+, root-lasso, and scad. Methods
are provided for the resulting class hdlm corresponding to
methods for the class lm.

Mimics the lm function found in the recommended package
stats to fit high dimensional regression models with point
estimates, standard errors, and p-values. Methods for printing
and summarizing the results are given.

Mimics the lm function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
summarizing the results are given.
R (>= 2.14), glmnet, foreach, MASS, iterators

R (>= 3.0), glmnet, foreach, MASS, iterators

Mimics the lm() function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
summarizing the results are given.
R (>= 3.0), glmnet, foreach, MASS, iterators, Matrix

c(person("Martin", "Spindler", email="spindler@mea.mpisoc.mpg.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.
Martin Spindler [cre, aut],
Victor Chernozhukov [aut],
Christian Hansen [aut]