Implementation of the automatic shift detection method for
    Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
    phylogenies. Some tools to handle equivalent shifts configurations are also
    available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
    Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.

Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.

Contains functions for developing phylogenetic trees as
    deeply-nested lists ("dendrogram" objects).
    Enables bi-directional conversion between dendrogram and
    "phylo" objects
    (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>),
    and features several tools for command-line tree
    manipulation and import/export via Newick parenthetic text.

Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.

Provides functions to estimate statistical errors of phylogenetic
    metrics particularly to detect binary trait influence on diversification, as
    well as a function to simulate trees with fixed number of sampled taxa and trait
    prevalence.

Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>, Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007) <doi:10.1002/ece3.3093>.