README.Rmd

---
output: github_document
bibliography: ./inst/REFERENCES.bib
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
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# hdme
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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.
```{r, eval=FALSE}
install.packages("hdme")
```

You can install the latest development version from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("osorensen/hdme", build_vignettes = TRUE)
```


### Dependency on Rglpk

`hdme` uses the [Rglpk package](https://cran.r-project.org/package=Rglpk), 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](https://github.com/osorensen/hdme/issues). Code contribution by pull requests are also appreciated.

## Citation

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

```{r}
citation("hdme")
```


## References