ArticleBMC bioinformatics2024
Piikun: an information theoretic toolkit for analysis and visualization of species delimitation metric space.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Revision of the genusZooKeys · 2025Article
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2 authors.
Funding
Abstract
backgroundExisting software for comparison of species delimitation models do not provide a (true) metric or distance functions between species delimitation models, nor a way to compare these models in terms of relative clustering differences along a lattice of partitions.
resultsPiikun is a Python package for analyzing and visualizing species delimitation models in an information theoretic framework that, in addition to classic measures of information such as the entropy and mutual information [1], provides for the calculation of the Variation of Information (VI) criterion [2], a true metric or distance function for species delimitation models that is aligned with the lattice of partitions.
conclusionsPiikun is available under the MIT license from its public repository ( https://github.com/jeetsukumaran/piikun ), and can be installed locally using the Python package manager 'pip'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.