Nothing

```
#' Direct an undirected graph.
#'
#' Starting from a \code{root} not, directs all arcs away from it and applies
#' the same, recursively to its children and descendants. Produces a directed
#' forest.
#'
#' @param g An undirected graph.
#' @param root A character. Optional tree root.
#' @return A directed graph
#' @keywords internal
direct_forest <- function(g, root = NULL) {
graph_direct_forest(g, root = NULL)
}
#' Direct an undirected graph.
#'
#' The graph must be connected and the function produces a directed tree.
#' @return A graph. The directed tree.
#' @keywords internal
direct_tree <- function(g, root = NULL) {
graph_direct_tree(g, root)
}
direct_graph <- function(g) {
graph_direct(g)
}
#' Returns the undirected augmenting forest.
#'
#' Uses Kruskal's algorithm to find the augmenting forest that maximizes the sum
#' of pairwise weights. When the weights are class-conditional mutual
#' information this forest maximizes the likelihood of the tree-augmented naive
#' Bayes network.
#'
#' If \code{g} is not connected than this will return a forest; otherwise it is
#' a tree.
#'
#' @param g A graph. The undirected graph with pairwise
#' weights.
#' @return A graph. The maximum spanning forest.
#' @references Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network
#' classifiers. \emph{Machine Learning}, \bold{29}, pp. 131--163.
#'
#' Murphy KP (2012). \emph{Machine learning: a probabilistic perspective}. The
#' MIT Press. pp. 912-914.
#' @keywords internal
max_weight_forest <- function(g) {
graph_max_weight_forest(g)
}
#' Merges multiple disjoint graphs into a single one.
#'
#' @param g A graph
#' @return A graph
#' @keywords internal
graph_union <- function(g) {
graph_internal_union(g)
}
# Adds a node to DAG as root and parent of all nodes.
superimpose_node <- function(dag, node) {
graph_superimpose_node(dag, node)
}
is_dag_graph <- function(dag) {
graph_is_dag(dag)
}
check_node <- function(node) {
stopifnot(assertthat::is.string(node))
}
#' Returns a naive Bayes structure
#'
#' @keywords internal
nb_dag <- function(class, features) {
anb_make_nb(class, features)
}
```

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