Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN version CRAN RStudio mirror downloads Total CRAN downloads

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 (Comprehensive R Archive Network). It can be easily be installed from within R by running

install.packages("rags2ridges")

If you wish to install the latest version of rags2ridges directly from the master branch here at GitHub, run

#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 if you wish to install the package from the source.

Visit CRAN, the documentation site, 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.

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).
  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).
  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).
  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).
  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).

Please cite the relevant publications if you use rags2ridges.