Implements measures of tree similarity, including 
  information-based generalized Robinson-Foulds distances
  (Phylogenetic Information Distance, Clustering Information Distance,
  Matching Split Information Distance; Smith 2020)
  <doi:10.1093/bioinformatics/btaa614>; 
  Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
  <doi:10.1007/978-3-642-40453-5_13>, 
  including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
  the Matching Split Distance (Bogdanowicz & Giaro 2012)
  <doi:10.1109/TCBB.2011.48>;
  Maximum Agreement Subtree distances;
  the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
  Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. 
  (1996) <doi:10.1007/3-540-61332-3_168>.
  Includes tools for visualizing mappings of tree space (Smith 2022)
  <doi:10.1093/sysbio/syab100>,
  for identifying islands of trees (Silva and Wilkinson 2021)
  <doi:10.1093/sysbio/syab015>,
  for calculating the median of sets of trees,
  and for computing the information content of trees and splits.

Perform two types of analysis: 1) checking the
    goodness-of-fit of tree models to your single-cell gene expression
    data; and 2) deciding which tree best fits your data.

Creates interpretable decision tree visualizations 
    with the data represented as a heatmap at the tree's leaf nodes.
    'treeheatr' utilizes the customizable 'ggparty' package for 
    drawing decision trees.

A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.

Provides 'ggplot2' geoms for drawing treemaps.

Implementation of unconditional Bernoulli Scan Statistic developed
             by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> 
             for hierarchical tree structures. Tree-based Scan Statistics are an
             exploratory method to identify event clusters across the space of a 
             hierarchical tree.

Provides functionality for creation and comparison of polynomials that uniquely
  describe trees as introduced in Liu (2019, <arXiv:1904.03332>). The core method
  converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials 
  described with coefficient matrices. Additionally, a conversion for rooted binary trees 
  with binary trait labels is also provided. Once the polynomials of trees are calculated 
  there are functions to calculate distances, distance matrices and plot different distance 
  trees from a target tree. Manipulation and conversion to the tree defining polynomials is 
  implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 
  'RcppThread' is used to improve performance converting to polynomials and calculating distances.