TY - JOUR
T1 - rags2ridges
T2 - A One-Stop-ℓ2-Shop for Graphical Modeling of High-Dimensional Precision Matrices
AU - Peeters, Carel F. W.
AU - Bilgrau, Anders Ellern
AU - van Wieringen, Wessel N.
N1 - Funding Information: This research was partly supported by Grant FP7-269553 (EpiRadBio) through the European Community’s Seventh Framework Programme (FP7, 2007–2013). The Authors would like to thank two anonymous Referees whose constructive comments have led to an improvement in presentation. Publisher Copyright: © 2022, American Statistical Association.
PY - 2022
Y1 - 2022
N2 - A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices through ridge (ℓ2) penalties. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-ℓ2-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer’s disease.
AB - A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices through ridge (ℓ2) penalties. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-ℓ2-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer’s disease.
KW - R
KW - graphical modeling
KW - high-dimensional data
KW - networks
KW - regularization
UR - http://www.scopus.com/inward/record.url?scp=85132644177&partnerID=8YFLogxK
U2 - https://doi.org/10.18637/jss.v102.i04
DO - https://doi.org/10.18637/jss.v102.i04
M3 - Article
SN - 1548-7660
VL - 102
JO - Journal of Statistical Software
JF - Journal of Statistical Software
ER -