https://github.com/cfwp/rags2ridges
Get ridge or die trying - 2 cents
https://github.com/cfwp/rags2ridges
c-plus-plus cran graphical-models machine-learning networkscience r statistics
Last synced: 8 months ago
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Get ridge or die trying - 2 cents
- Host: GitHub
- URL: https://github.com/cfwp/rags2ridges
- Owner: CFWP
- Created: 2015-01-15T10:00:07.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2025-08-29T15:59:06.000Z (10 months ago)
- Last Synced: 2025-09-08T14:19:37.620Z (9 months ago)
- Topics: c-plus-plus, cran, graphical-models, machine-learning, networkscience, r, statistics
- Language: R
- Homepage:
- Size: 8.25 MB
- Stars: 8
- Watchers: 6
- Forks: 2
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://www.repostatus.org/#active)
[](https://cran.r-project.org/package=rags2ridges)
[](https://cran.r-project.org/package=rags2ridges/index.html)
[](https://www.r-pkg.org/pkg/rags2ridges)
**rags2ridges**
---------------
The R-package **rags2ridges** performs L2-penalized estimation of precison (and covariance) matrices.
The package contains *proper* L2-penalized maximum-likelihood estimators for the precision matrix as well as supporting functions to employ these estimators in a (integrative or meta-analytic) graphical modeling setting. The package has a modular setup and features fast and efficient algorithms.
## Installation
The released and tested version of **rags2ridges** is available at
[CRAN](https://cran.r-project.org/package=rags2ridges) (Comprehensive R Archive Network). It can be easily be installed from within R by running
```R
install.packages("rags2ridges")
```
If you wish to install the latest version of **rags2ridges** directly from the master branch here at GitHub, run
```R
#install.packages("remotes") # Uncomment if not installed
remotes::install_github("CFWP/rags2ridges")
```
Note, that this version is in development and is different from the version at CRAN. As such, it may be unstable. Be sure that you have the
[package development prerequisites](https://support.posit.co/hc/en-us/articles/200486498-Package-Development-Prerequisites) if you wish to install the package from the source.
Visit [CRAN](https://cran.r-project.org/package=rags2ridges/news/news.html), [the documentation site](https://cfwp.github.io/rags2ridges/news/index.html), or run `news(package = "rags2ridges")` after installation to view the latest notable changes to **rags2ridges**.
For previous versions of **rags2ridges**, visit the [archive at CRAN.](https://cran.r-project.org/src/contrib/Archive/rags2ridges/)
## Usage and getting started
The `vignette("rags2ridges")` provides a light introduction to **rags2ridges** and details how to quickly get started.
## References
Relevant publications to **rags2ridges** include (ordered according to year):
1. Peeters, C.F.W., Bilgrau, A.E., & van Wieringen, W.N. (2022).
*"rags2ridges: A One-Stop-l2-Shop for Graphical Modeling of High-Dimensional Precision Matrices"*.
Journal of Statistical Software, vol. 102(4):1-32.
([doi:10.18637/jss.v102.i04](https://doi.org/10.18637/jss.v102.i04)).
2. Peeters, C.F.W., van de Wiel, M.A., & van Wieringen, W.N. (2020)
*"The Spectral Condition Number Plot for Regularization Parameter Evaluation"*,
Computational Statistics, vol. 35:629-646
([doi:10.1007/s00180-019-00912-z](https://doi.org/10.1007/s00180-019-00912-z)).
3. Bilgrau\*, A.E., Peeters\*, C.F.W., Eriksen, P.S., Boegsted, M., & van Wieringen, W.N. (2020).
*"Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes"*,
Journal of Machine Learning Research, vol. 21(26):1-52
([PDF](https://www.jmlr.org/papers/volume21/15-509/15-509.pdf)).
4. van Wieringen, W.N. & Peeters, C.F.W. (2016).
*"Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data"*,
Computational Statistics & Data Analysis, vol. 103:284-303
([doi:10.1016/j.csda.2016.05.012](https://doi.org/10.1016/j.csda.2016.05.012)).
5. van Wieringen, W.N. & Peeters, C.F.W. (2015).
*"Application of a New Ridge Estimator of the Inverse Covariance Matrix
to the Reconstruction of Gene-Gene Interaction Networks"*.
In: di Serio, C., Lio, P., Nonis, A., and Tagliaferri, R. (Eds.)
`Computational Intelligence Methods for Bioinformatics and Biostatistics'.
Lecture Notes in Computer Science, vol. 8623. Springer, pp. 170-179
([doi:10.1007/978-3-319-24462-4_15](https://doi.org/10.1007/978-3-319-24462-4_15)).
Please cite the relevant publications if you use **rags2ridges**.