The goal of hdme is to provide penalized regression methods for High-Dimensional Measurement Error problems (errors-in-variables).


Install hdme from CRAN using.


You can install the latest development version from github with:

# install.packages("devtools")

Note when installing on macOS

The package Rglpk is suggested when installing hdme. In order to install Rglpk on macOS, you may need to first install GLPK by issuing the following statement on the command line:

brew install glpk

Then install Rglpk:


If you are not able to install Rglpk, then please install the suggested package lpSolveAPI instead, using the command


The functions in hdme that use Rglpk, will switch to lpSolveAPI automatically if the former is not available. When either Rglpk or lpSolveAPI is installed, then install the development version of hdme using



hdme provides implementations of the following algorithms:

The methods implemented in the package include


James, Gareth M., and Peter Radchenko. 2009. “A Generalized Dantzig Selector with Shrinkage Tuning.” Biometrika 96 (2): 323–37.

Loh, Po-Ling, and Martin J. Wainwright. 2012. “High-Dimensional Regression with Noisy and Missing Data: Provable Guarantees with Nonconvexity.” Ann. Statist. 40 (3). The Institute of Mathematical Statistics: 1637–64.

Rosenbaum, Mathieu, and Alexandre B. Tsybakov. 2010. “Sparse Recovery Under Matrix Uncertainty.” Ann. Statist. 38 (5): 2620–51.

Sorensen, Oystein, Arnoldo Frigessi, and Magne Thoresen. 2015. “Measurement Error in Lasso: Impact and Likelihood Bias Correction.” Statistica Sinica 25 (2). Institute of Statistical Science, Academia Sinica: 809–29.

Sorensen, Oystein, Kristoffer Herland Hellton, Arnoldo Frigessi, and Magne Thoresen. 2018. “Covariate Selection in High-Dimensional Generalized Linear Models with Measurement Error.” Journal of Computational and Graphical Statistics. Taylor & Francis.