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#' TreeDist: Distances between Phylogenetic Trees
#'
#' 'TreeDist' is an R package that implements a suite of metrics that quantify the
#' topological distance between pairs of unweighted phylogenetic trees.
#' It also includes a simple "Shiny" application to allow the visualization of
#' distance-based tree spaces, and functions to calculate the information content
#' of trees and splits.
#'
#' "TreeDist" primarily employs metrics in the category of
#' "generalized Robinson–Foulds distances": they are based on comparing splits
#' (bipartitions) between trees, and thus reflect the relationship data within
#' trees, with no reference to branch lengths.
#' Detailed documentation and usage instructions are
#' [available online](https://ms609.github.io/TreeDist/) or in the vignettes.
#'
#'
#' ## Generalized RF distances
#'
#' The [Robinson–Foulds distance](https://ms609.github.io/TreeDist/articles/Robinson-Foulds.html)
#' simply tallies the number of non-trivial splits (sometimes inaccurately
#' termed clades, nodes or edges) that occur in both trees -- any splits that are
#' not perfectly identical contributes one point to the distance score of zero,
#' however similar or different they are.
#' By overlooking potential similarities between almost-identical splits,
#' this conservative approach has undesirable properties.
#'
#' ["Generalized" RF metrics](https://ms609.github.io/TreeDist/articles/Generalized-RF.html)
#' generate _matchings_ that pair each split in one tree with a similar split
#' in the other.
#' Each pair of splits is assigned a similarity score; the sum of these scores in
#' the optimal matching then quantifies the similarity between two trees.
#'
#' Different ways of calculating the the similarity between a pair of splits
#' lead to different tree distance metrics, implemented in the functions below:
#'
#' * [`MutualClusteringInfo()`](https://ms609.github.io/TreeDist/reference/TreeDistance.html), [`SharedPhylogeneticInfo()`](https://ms609.github.io/TreeDist/reference/TreeDistance.html)
#'
#' + Smith (2020) scores matchings based on the amount of information
#' that one partition contains about the other. The Mutual Phylogenetic
#' Information assigns zero similarity to split pairs that cannot
#' both exist on a single tree; The Mutual
#' Clustering Information metric is more forgiving, and exhibits more
#' desirable behaviour; it is the recommended metric for tree comparison.
#' (Its complement, [`ClusteringInfoDistance()`](https://ms609.github.io/TreeDist/reference/TreeDistance.html), returns a tree
#' distance.)
#'
#' * [`NyeSimilarity()`](https://ms609.github.io/TreeDist/reference/NyeSimilarity.html)
#'
#' + Nye _et al._ (2006) score matchings according to the size of the largest
#' split that is consistent with both of them, normalized against
#' the Jaccard index. This approach is extended by Böcker _et al_. (2013)
#' with the Jaccard–Robinson–Foulds metric (function
#' [`JaccardRobinsonFoulds()`](https://ms609.github.io/TreeDist/reference/JaccardRobinsonFoulds.html)).
#'
#' * [`MatchingSplitDistance()`](https://ms609.github.io/TreeDist/reference/MatchingSplitDistance.html)
#'
#' + Bogdanowicz and Giaro (2012) and Lin _et al._ (2012) independently proposed
#' counting the number of "mismatched" leaves in a pair of splits.
#' [`MatchingSplitInfoDistance()`](https://ms609.github.io/TreeDist/reference/TreeDistance.html)
#' provides an information-based equivalent (Smith 2020).
#'
#'
#' The package also implements the variation of the path distance
#' proposed by Kendal and Colijn (2016) (function
#' [`KendallColijn()`](https://ms609.github.io/TreeDist/reference/KendallColijn.html)),
#' approximations of the Nearest-Neighbour Interchange (NNI) distance (function
#' [`NNIDist()`](https://ms609.github.io/TreeDist/reference/NNIDist.html);
#' following Li _et al._ (1996)), and calculates the size (function
#' [`MASTSize()`](https://ms609.github.io/TreeDist/reference/MASTSize.html)) and
#' information content (function
#' [`MASTInfo()`](https://ms609.github.io/TreeDist/reference/MASTSize.html)) of the
#' Maximum Agreement Subtree.
#'
#' For an implementation of the Tree Bisection and Reconnection (TBR) distance, see
#' the package '[TBRDist](https://ms609.github.io/TBRDist/index.html)'.
#'
#'
#' # Tree space analysis
#'
#' Map tree spaces and readily visualize mapped landscapes, avoiding
#' common analytical pitfalls (Smith, forthcoming),
#' using the inbuilt graphical user interface:
#'
#' ```r
#' TreeDist::MapTrees()
#' ```
#'
#' Serious analysts should consult the
#' [vignette](https://ms609.github.io/TreeDist/articles/treespace.html)
#' for a command-line interface.
#'
#'
#' @seealso
#'
#' Further documentation is available in the
#' [package vignettes](https://ms609.github.io/TreeDist/articles/), visible from
#' R using `vignette(package = "TreeDist")`.
#'
#' Other R packages implementing tree distance functions include:
#'
#' * [ape](http://ape-package.ird.fr/):
#' - `cophenetic.phylo()`: Cophenetic distance
#' - `dist.topo()`: Path (topological) distance, Robinson–Foulds distance.
#' * [phangorn](https://cran.r-project.org/package=phangorn)
#' - `treedist()`: Path, Robinson–Foulds and approximate SPR distances.
#' * [Quartet](https://ms609.github.io/Quartet/): Triplet and Quartet distances,
#' using the tqDist algorithm.
#' * [TBRDist](https://ms609.github.io/TBRDist/): TBR and SPR distances on
#' unrooted trees, using the 'uspr; C library.
#' * [distory](https://cran.r-project.org/package=distory) (unmaintained):
#' Geodesic distance
#'
#' @references
#'
#' - \insertRef{Bocker2013}{TreeDist}
#'
#' - \insertRef{Bogdanowicz2012}{TreeDist}
#'
#' - \insertRef{Kendall2016}{TreeDist}
#'
#' - \insertRef{Li1996}{TreeDist}
#'
#' - \insertRef{Lin2012}{TreeDist}
#'
#' - \insertRef{Nye2006}{TreeDist}
#'
#' - \insertRef{SmithDist}{TreeDist}
#'
#' - \insertRef{SmithSpace}{TreeDist}
#'
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