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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).
Citation | hdme citation info |
github.com/osorensen/hdme |
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