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.