Functions to access the database of 217 data-frames with aggregate 
    study-level characteristics (that may act as effect modifiers) extracted 
    from published systematic reviews with network meta-analysis. The database shall 
    only be used for developing and appraising the methodology to assess 
    the transitivity assumption quantitatively.

Better looking call stacks after an error.

'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'Tracer' (<https://github.com/beast-dev/tracer/>) is a GUI tool 
  to parse and analyze the files generated by 'BEAST2'.
  This package provides a way to parse and analyze 'BEAST2' input
  files without active user input, but using
  R function calls instead.

Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.

Functions for preparing and analyzing animal tracking data, 
    with the intention of identifying areas which are potentially important at 
    the population level and therefore of conservation interest. Areas identified 
    using this package may be checked against global or regionally-defined criteria,
    such as those set by the Key Biodiversity Area program. The method
    published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.

Provides data cleaning and preprocessing tools for high-frequency
    positional data from real-time location tracking systems (UWB, RFID, and
    similar technologies), with functions for ID mapping, time period marking,
    data standardization, and two-phase conditional gap interpolation.
    See Bilevicius (2026) <doi:10.5281/zenodo.20783488>.

Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in  evolutionary and ecological studies.