  In this article the package High-dimensional Metrics (\texttt{hdm}) is
introduced. It is a collection of statistical methods for estimation and
quantification of uncertainty in high-dimensional approximately sparse models.
It focuses on providing confidence intervals and significance testing for
(possibly many) low-dimensional subcomponents of the high-dimensional parameter
vector. Efficient estimators and uniformly valid confidence intervals for
regression coefficients on target variables (e.g., treatment or policy
variable) in a high-dimensional approximately sparse regression model, for
average treatment effect (ATE) and average treatment effect for the treated
(ATET), as well for extensions of these parameters to the endogenous setting
are provided. Theory grounded, data-driven methods for selecting the
penalization parameter in Lasso regressions under heteroscedastic and
non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence
intervals for regression coefficients of a high-dimensional sparse regression
are implemented. Data sets which have been used in the literature and might be
useful for classroom demonstration and for testing new estimators are included.
