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

Install `hdme`

from CRAN using.

`install.packages("hdme")`

You can install the latest development version from github with:

```
# install.packages("devtools")
devtools::install_github("osorensen/hdme")
```

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`

:

`install.packages("Rglpk")`

If you are not able to install `Rglpk`

, then please install the suggested package `lpSolveAPI`

instead, using the command

`install.packages("lpSolveAPI")`

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

`devtools::install_github("osorensen/hdme")`

hdme provides implementations of the following algorithms:

The methods implemented in the package include

- Corrected Lasso for Linear Models (Loh and Wainwright (2012))
- Corrected Lasso for Generalized Linear Models (Sorensen, Frigessi, and Thoresen (2015))
- Matrix Uncertainty Selector for Linear Models (Rosenbaum and Tsybakov (2010))
- Matrix Uncertainty Selector for Generalized Linear Models (Sorensen et al. (2018))
- Matrix Uncertainty Lasso for Generalized Linear Models (Sorensen et al. (2018))
- Generalized Dantzig Selector (James and Radchenko (2009))

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.