Repository files navigation

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

Installhdme from CRAN using.

install.packages("hdme")

You can install the latest development version from github with:

# install.packages("devtools")devtools::install_github("osorensen/hdme",build_vignettes=TRUE)

hdme uses theRglpkpackage, which requires theGLPK library package to be installed. On some platforms this requires amanual installation.

On Debian/Ubuntu, you might use:

sudo apt-get install libglpk-dev

On macOS, you might use:

brew install glpk

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 andTsybakov (2010))
• Matrix Uncertainty Selector for Generalized Linear Models (Sorensenet al. (2018))
• Matrix Uncertainty Lasso for Generalized Linear Models (Sorensen etal. (2018))
• Generalized Dantzig Selector (James and Radchenko (2009))

Contributions tohdme are very welcome. If you have a question orsuspect you have found a bug, pleaseopen anIssue. Code contribution bypull requests are also appreciated.

If using hdme in a scientific publication, please cite the followingpaper:

citation("hdme")#>#> To cite package 'hdme' in publications use:#>#>   Sorensen, (2019). hdme: High-Dimensional Regression with Measurement#>   Error. Journal of Open Source Software, 4(37), 1404,#>   https://doi.org/10.21105/joss.01404#>#> A BibTeX entry for LaTeX users is#>#>   @Article{,#>     title = {hdme: High-Dimensional Regression with Measurement Error},#>     journal = {Journal of Open Source Software},#>     volume = {4},#>     number = {37},#>     pages = {1404},#>     year = {2019},#>     doi = {10.21105/joss.01404},#>     author = {Oystein Sorensen},#>   }