Perform test to detect differences in structure between families of
             trees. The method is based on cophenetic distances and aggregated
             Student's tests.

Testing for trajectory presence and heterogeneity on
 multivariate data. Two statistical methods (Tenha & Song 2022) 
 <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
 test quantifies the statistical evidence for trajectory presence. The
 subset specificity measure summarizes pattern heterogeneity using the
 minimum subtree cover. There is no user tunable parameters for either
 method. Examples are included to illustrate how to use the methods on 
 single-cell data for studying gene and pathway expression dynamics and
 pathway expression specificity.

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 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.