Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.

Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.

Diversification is one of the most important concepts in portfolio management. This framework offers scholars, practitioners and policymakers a useful toolbox to measure diversification. Specifically, this framework provides recent diversification measures from the recent literature. These diversification measures are based on the works of Rudin and Morgan (2006) <doi:10.3905/jpm.2006.611807>, Choueifaty and Coignard (2008) <doi:10.3905/JPM.2008.35.1.40>, Vermorken et al. (2012) <doi:10.3905/jpm.2012.39.1.067>, Flores et al. (2017) <doi:10.3905/jpm.2017.43.4.112>, Calvet et al. (2007) <doi:10.1086/524204>, and Candelon, Fuerst and Hasse (2020).

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.

Implements interaction forests [1], which are specific diversity forests and 
  the basic form of diversity forests that uses univariable, binary splitting [2].
  Interaction forests (IFs) are ensembles of decision trees that model quantitative and
  qualitative interaction effects using bivariable splitting. IFs come with the 
  Effect Importance Measure (EIM), which can be used to identify variable pairs that 
  feature quantitative and qualitative interaction effects with high predictive
  relevance. IFs and EIM focus on well interpretable forms of interactions.
  The package also offers plot functions for visualising the estimated forms of 
  interaction effects.
  Categorical, metric, and survival outcomes are supported.
  This is a fork of the R package 'ranger' (main author: Marvin N. Wright) that 
  implements random forests using an efficient C++  implementation.
  References:
  [1] Hornung, R. & Boulesteix, A.-L. (2022) Interaction Forests: Identifying and 
      exploiting interpretable quantitative and qualitative interaction effects.
	  Computational Statistics & Data Analysis 171:107460, <doi:10.1016/j.csda.2022.107460>.
  [2] Hornung, R. (2022) Diversity forests: Using split sampling to enable 
      innovative complex split procedures in random forests.
	  SN Computer Science 3(2):1, <doi:10.1007/s42979-021-00920-1>.