A comprehensive and easy to use R implementation of confirmatory
    phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer
    (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.

Phylogenetic Diversity (PD, Faith 1992), Evolutionary Distinctiveness (ED, Isaac et al. 2007), Phylogenetic Endemism (PE, Rosauer et al. 2009; Laffan et al. 2016), and Weighted Endemism (WE, Laffan et al. 2016) for presence-absence raster.
    Faith, D. P. (1992) <doi:10.1016/0006-3207(92)91201-3>
    Isaac, N. J. et al. (2007) <doi:10.1371/journal.pone.0000296>
    Laffan, S. W. et al. (2016)  <doi:10.1111/2041-210X.12513>
    Rosauer, D. et al. (2009) <doi:10.1111/j.1365-294X.2009.04311.x>.

Computational infrastructure for biogeography, community ecology,
    and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>.
    It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>.
    The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013>
    on patterns and processes of biogeographical regionalization. Additionally, the package
    contains fast and efficient functions to compute more standard conservation measures
    such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness
    and global endangerment, as well as compositional turnover (e.g., beta diversity).

Implements novel tools for estimating sample sizes needed for phylogenetic studies, including
    studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023).

Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using 
    structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>.  
    This implementation includes a minimal set of features, to 
    allow users to easily read all of the documentation and source code.  PCM using SEM 
    includes phylogenetic linear models and structural equation models as nested submodels, 
    but also allows imputation of missing values.  Features and comparison with other packages
    are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.