May 2019
hdme: High-Dimensional Regression with Measurement Error
This paper presents the hdme package, implementing variable selection methods for regression with covariate measurement error.
Jun 2018
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
This paper proposes an extension of the generalized Dantzig selector for cases with measurement error in the predictor variables.
Mar 2018
hdme: High-Dimensional Regression with Measurement Error
This R package contains functions for fitting variable selection models in the presence of noise in the predictor variables. In particular, it supports a corrected lasso and the generalized matrix uncertainty selector. In addition, it offers an implementation of the (generalized) Dantzig selector.