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      Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

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      Trends in Ecology & Evolution
      Elsevier BV

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          BEAST: Bayesian evolutionary analysis by sampling trees

          Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.
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            Feature Selection with theBorutaPackage

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              Environmental DNA metabarcoding: Transforming how we survey animal and plant communities

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                Author and article information

                Contributors
                Journal
                Trends in Ecology & Evolution
                Trends in Ecology & Evolution
                Elsevier BV
                01695347
                August 2023
                August 2023
                : 38
                : 8
                : 760-772
                Article
                10.1016/j.tree.2023.03.011
                270a149f-37b5-43c8-8775-b34cedbb99cf
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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