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hdme

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

Installation

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)

Dependency on Rglpk

hdme uses theRglpk package, whichrequires the GLPK library package to be installed. On some platformsthis 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 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

Contributions tohdme are very welcome. If you have aquestion or suspect you have found a bug, pleaseopen an Issue. Codecontribution by pull requests are also appreciated.

Citation

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},#>   }

References

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

Loh, Po-Ling, and Martin J. Wainwright. 2012. “High-DimensionalRegression with Noisy and Missing Data: Provable Guarantees withNonconvexity.”Ann. Statist. 40 (3): 1637–64.

Rosenbaum, Mathieu, and Alexandre B. Tsybakov. 2010. “Sparse RecoveryUnder 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, andMagne Thoresen. 2018. “Covariate Selection in High-DimensionalGeneralized Linear Models with Measurement Error.”Journal ofComputational and Graphical Statistics 27 (4): 739–49.https://doi.org/10.1080/10618600.2018.1425626.

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