In osorensen/hdme: High-Dimensional Regression with Measurement Error

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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:

```{sh, eval=FALSE} sudo apt-get install libglpk-dev

On macOS, you might use:

```{sh, eval=FALSE}
brew install glpk

Methods

hdme provides implementations of the following algorithms:

The methods implemented in the package include

• Corrected Lasso for Linear Models (@loh2012)
• Corrected Lasso for Generalized Linear Models (@sorensen2015)
• Matrix Uncertainty Selector for Linear Models (@rosenbaum2010)
• Matrix Uncertainty Selector for Generalized Linear Models (@sorensen2018)
• Matrix Uncertainty Lasso for Generalized Linear Models (@sorensen2018)
• Generalized Dantzig Selector (@james2009)

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")

References

osorensen/hdme documentation built on May 18, 2023, 11:35 p.m.