Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.

Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, 
    Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, 
    Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, &
    Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy 
    Decomposition. Insights into Hominin Occupation and Carcass Processing 
    at Qesem cave".

Implementation of three methods based on the diversity forest (DF) algorithm 
  (Hornung, 2022, <doi:10.1007/s42979-021-00920-1>), a split-finding approach that 
  enables complex split procedures in random forests.
  The package includes:
    1. Interaction forests (IFs) (Hornung & Boulesteix, 2022, <doi:10.1016/j.csda.2022.107460>): 
    Model quantitative and qualitative interaction effects using bivariable splitting. 
    Come with the Effect Importance Measure (EIM), which can be used to identify variable 
    pairs that have well-interpretable quantitative and qualitative interaction effects 
    with high predictive relevance.
	2. Two random forest-based variable importance measures (VIMs) for multi-class outcomes: 
	the class-focused VIM, which ranks covariates by their ability to distinguish individual 
	outcome classes from the others, and the discriminatory VIM, which measures overall 
	covariate influence irrespective of class-specific relevance.
    3. The basic form of diversity forests that uses conventional univariable, binary 
    splitting (Hornung, 2022).
  Except for the multi-class VIMs, all methods support categorical, metric, and survival 
  outcomes. The package includes visualization tools for interpreting the identified 
  covariate effects.
  Built as a fork of the 'ranger' R package (main author: Marvin N. Wright), which 
  implements random forests using an efficient C++ implementation.