hdme

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

Installation

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", build_vignettes = TRUE)

Dependency on Rglpk

hdme uses the Rglpk package, which requires the GLPK library package to be installed. On some platforms this requires a manual installation.

On Debian/Ubuntu, you might use:

sudo apt-get install libglpk-dev

On macOS, you might use:

brew install glpk

Methods

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))

Contributions

Contributions to hdme are very welcome. If you have a question or suspect you have found a bug, please open an Issue. Code contribution by pull requests are also appreciated.

Citation

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

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},
#>   }

References

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): 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): 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 27 (4): 739–49. https://doi.org/10.1080/10618600.2018.1425626.