car, corpcor, corrplot, dplyr, NetworkToolbox,
NetworkComparisonTest, nlshrink, rmarkdown, knitr

Estimate non-regularized Gaussian graphical models, Ising models,
and mixed graphical models. The current methods consist of multiple regression,
a non-parametric bootstrap  <doi:10.1080/00273171.2019.1575716>, and Fisher z
transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty,
predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.
Rdpack, bestglm, GGally, network, sna, Matrix, poibin,
parallel, doParallel, foreach, corpcor, psych, MASS, stats,
methods, ggplot2, GGMncv

Gaussian Graphical Models Using Ridge Penalty Followed by
Thresholding and Reestimation
Min Jin Ha [aut, cre], Shannon Holloway [ctb]
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).

Min Jin Ha [aut, cre], Shannon T. Holloway [ctb]
Estimation of partial correlation matrix using ridge penalty
followed by thresholding and reestimation. Under multivariate Gaussian
assumption, the matrix constitutes an Gaussian graphical model (GGM).

Annie Bouvier, Christophe Giraud, Sylvie Huet, Verzelen N.
GGMselect is a package dedicated to graph estimation in
Gaussian Graphical Models. The main functions return the
adjacency matrix of an undirected graph estimated from a data
matrix.
mvtnorm, lars, mvbutils (>= 2.0), gtools

Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.

Orphaned on 2018-02-01 as installation issues were not
corrected despite reminders.

Orphaned on 2018-02-01 as installation issues were not
corrected despite reminders.

Attempted to use SAFE_FFLAGS as part of PKG_LIBS.

Graph estimation in Gaussian Graphical Models, following the method
developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.
The main functions return the adjacency matrix of an undirected graph
estimated from a data matrix.