`sample_tree()`

generates a random with a given number of nodes uniform
at random from the set of labelled trees.

## Usage

`sample_tree(n, directed = FALSE, method = c("lerw", "prufer"))`

## Arguments

- n
The number of nodes in the tree

- directed
Whether to create a directed tree. The edges of the tree are oriented away from the root.

- method
The algorithm to use to generate the tree. ‘prufer’ samples Prüfer sequences uniformly and then converts the sampled sequence to a tree. ‘lerw’ performs a loop-erased random walk on the complete graph to uniformly sampleits spanning trees. (This is also known as Wilson's algorithm). The default is ‘lerw’. Note that the method based on Prüfer sequences does not support directed trees at the moment.

## Details

In other words, the function generates each possible labelled tree with the given number of nodes with the same probability.

## See also

Random graph models (games)
`erdos.renyi.game()`

,
`sample_()`

,
`sample_bipartite()`

,
`sample_chung_lu()`

,
`sample_correlated_gnp()`

,
`sample_correlated_gnp_pair()`

,
`sample_degseq()`

,
`sample_dot_product()`

,
`sample_fitness()`

,
`sample_fitness_pl()`

,
`sample_forestfire()`

,
`sample_gnm()`

,
`sample_gnp()`

,
`sample_grg()`

,
`sample_growing()`

,
`sample_hierarchical_sbm()`

,
`sample_islands()`

,
`sample_k_regular()`

,
`sample_last_cit()`

,
`sample_pa()`

,
`sample_pa_age()`

,
`sample_pref()`

,
`sample_sbm()`

,
`sample_smallworld()`

,
`sample_traits_callaway()`