This package includes some implementations of Bayesian Gaussian Graphical Models based on the generalized approach proposed by Franco et al. (under review). Currently, there are 8 models implemented:
- Priors with one parameter for regularization (i.e., models that control the regularization with a single parameter):
"normal"
,"laplace"
,"logistic"
,"cauchy"
, and"hypersec"
. Respectively, these values set a normal, laplace, logistic, Cauchy, or hyperbolic secant as prior distributions; - Priors with two parameters for regularization (i.e., models that control the regularization with two parameters: a "regularization" parameter per se and an "heavy-tailedness" parameter, which may be useful when there are "outlier" correlations):
"t"
,"lomax"
, and"NEG"
. Respectively, these values set a t, double lomax, or normal-exponential-gamma as prior distributions.
The user can also decide if they will estimate a sparse or non-sparse network. The estimation of the BGGMs is done with the bggm
function.
Using the 'remotes' package:
install.packages("remotes")
remotes::install_github("vthorrf/gbggm")