software
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.
Description
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).
Background
For more background, see the paper on measurement error in lasso and the paper on the generalized matrix uncertainty selector.