Matthew W Pennell1, Jonathan M Eastman1, Graham J Slater1, Joseph W Brown2, Josef C Uyeda1, Richard G FitzJohn1, Michael E Alfaro1, Luke J Harmon1. 1. Department of Biological Sciences and Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA, Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia and Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA. 2. Department of Biological Sciences and Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA, Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia and Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USADepartment of Biological Sciences and Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA, Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia and Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Abstract
SUMMARY: Phylogenetic comparative methods are essential for addressing evolutionary hypotheses with interspecific data. The scale and scope of such data have increased dramatically in the past few years. Many existing approaches are either computationally infeasible or inappropriate for data of this size. To address both of these problems, we present geiger v2.0, a complete overhaul of the popular R package geiger. We have reimplemented existing methods with more efficient algorithms and have developed several new approaches for accomodating heterogeneous models and data types. AVAILABILITY AND IMPLEMENTATION: This R package is available on the CRAN repository http://cran.r-project.org/web/packages/geiger/. All source code is also available on github http://github.com/mwpennell/geiger-v2. geiger v2.0 depends on the ape package. CONTACT: mwpennell@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Phylogenetic comparative methods are essential for addressing evolutionary hypotheses with interspecific data. The scale and scope of such data have increased dramatically in the past few years. Many existing approaches are either computationally infeasible or inappropriate for data of this size. To address both of these problems, we present geiger v2.0, a complete overhaul of the popular R package geiger. We have reimplemented existing methods with more efficient algorithms and have developed several new approaches for accomodating heterogeneous models and data types. AVAILABILITY AND IMPLEMENTATION: This R package is available on the CRAN repository http://cran.r-project.org/web/packages/geiger/. All source code is also available on github http://github.com/mwpennell/geiger-v2. geiger v2.0 depends on the ape package. CONTACT: mwpennell@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Martin Malmstrøm; Michael Matschiner; Ole K Tørresen; Bastiaan Star; Lars G Snipen; Thomas F Hansen; Helle T Baalsrud; Alexander J Nederbragt; Reinhold Hanel; Walter Salzburger; Nils C Stenseth; Kjetill S Jakobsen; Sissel Jentoft Journal: Nat Genet Date: 2016-08-22 Impact factor: 38.330