Screens and sorts phylogenetic trees in both traditional and
    extended Newick format. Allows for the fast and flexible screening (within
    a tree) of Exclusive clades that comprise only the target taxa and/or Non-
    Exclusive clades that includes a defined portion of non-target taxa.

Determine the chlorophyll a (Chl a) concentrations of different 
    phytoplankton groups based on their pigment biomarkers. The method uses
    non-negative matrix factorisation and simulated annealing to minimise error
    between the observed and estimated values of pigment concentrations
    (Hayward et al. (2023) <doi:10.1002/lom3.10541>).
    The approach is similar to the widely used 'CHEMTAX' program
    (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward,
    accurate, and not reliant on initial guesses for the pigment to Chl a
    ratios for phytoplankton groups.

Provides functions and example datasets for phytosociological 
    analysis, forest inventory, biomass and carbon estimation, and visualization 
    of vegetation data. Includes functions to compute structural parameters 
    [phytoparam(), summary.param(), stats()], estimate above-ground biomass 
    and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) 
    classes [stratvol()], generate collector and rarefaction curves [collector.curve(), 
    rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including 
    rectangular plots and individual coordinates]. Several example datasets are provided 
    to demonstrate the functionality of these tools. For more details see FAO 
    (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) 
    "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in 
    R: A routine to estimate phytosociological parameters" <doi:10.22533/at.ed.3552009033>.

A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.