Description: A package for probability propagation in graphical
independence networks, also known as probabilistic expert
systems (which includes Bayesian networks as a special case).

Depends: R (>= 2.1.0),methods,gRbase (>= 1.6-2),graph,RBGL

Depends: R (>= 2.15.2),methods,gRbase(>= 1.6-5),graph

Description: A package for probability propagation in graphical independence networks, also known as probabilistic expert systems or Bayesian networks.

Depends: R (>= 3.0.2), methods, gRbase (>= 1.7-0)

Description: Probability propagation in graphical independence networks, also known as probabilistic expert systems or Bayesian networks.
Depends: R (>= 3.0.2), methods, gRbase (>= 1.7-2)
Imports: igraph, graph, Rcpp (>= 0.11.1)
LinkingTo: Rcpp (>= 0.11.1), RcppArmadillo, RcppEigen, gRbase (>=

Description: Probability propagation in graphical independence networks, also known as Bayesian networks or probabilistic expert systems.

Description: Probability propagation in graphical independence networks, also
known as Bayesian networks or probabilistic expert systems.
Imports: igraph, graph, magrittr, functional, Rcpp (>= 0.11.1)

Imports: graph, Rgraphviz, igraph, magrittr, functional, Rcpp (>=
LinkingTo: Rcpp (>= 0.11.1), RcppArmadillo, RcppEigen, gRbase

Suggests: microbenchmark, knitr, testthat (>= 2.1.0)
Imports: graph, Rgraphviz, igraph, stats4, magrittr, Rcpp (>= 0.11.1)

Depends: R (>= 3.6.0), methods, gRbase (>= 1.8.6)

known as Bayesian networks or probabilistic expert systems. Documentation
of the package is provided in vignettes included in the package and in
the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>).

Imports: graph, Rgraphviz, igraph, stats4, broom, magrittr, Rcpp (>=

Title: Efficient selection of undirected graphical models for
Author: Gabriel Coelho Goncalves de Abreu <Gabriel.Abreu@agrsci.dk>,
Rodrigo Labouriau <Rodrigo.Labouriau@agrsci.dk>, David Edwards
Maintainer: Gabriel Coelho Goncalves de Abreu <Gabriel.Abreu@agrsci.dk>
Description: gRapHD is designed for efficient selection of
high-dimensional undirected graphical models. The package
provides tools for selecting trees, forests and decomposable
models minimizing information criteria such as AIC or BIC, and
for displaying the independence graphs of the models. It has
also some useful tools for analysing graphical structures. It
supports the use of discrete, continuous, or both types of