Functions for performing experimental comparisons of algorithms 
             using adequate sample sizes for power and accuracy. Implements the 
             methodology originally presented in Campelo and Takahashi (2019) 
             <doi:10.1007/s10732-018-9396-7> 
             for the comparison of two algorithms, and later generalised in 
             Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.

A first-principle, phylogeny-aware comparative genomics tool for 
             investigating associations between terms used to annotate genomic
             components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative 
             or rank variables such as number of cell types, genome size, or 
             density of specific genomic elements. See the project website for 
             more information, documentation and examples, and 
             <doi:10.1016/j.patter.2023.100728> for the full paper.

Fast 'C++'-backed tools for computing conspecific and total neighborhood basal area in mapped forest plots. Includes unweighted and distance-weighted neighborhoods, multiple radii, decay kernels, and basic edge correction. Outputs are model-ready covariates for forest competition, growth, and survival models, following neighborhood modeling workflows commonly used in spatial ecology (e.g., Hülsmann et al. 2024 <doi:10.1038/s41586-024-07118-4>).