yiheng-aug30 / hdme

R-package containing penalized regression methods for High-Dimensional Measurement Error problems (errors-in-variables)

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")
#> 
#>   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.