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Anne-Béatrice Dufour
Researcher at University of Lyon
Publications - 51
Citations - 8411
Anne-Béatrice Dufour is an academic researcher from University of Lyon. The author has contributed to research in topics: Principal component analysis & Population. The author has an hindex of 22, co-authored 51 publications receiving 7229 citations. Previous affiliations of Anne-Béatrice Dufour include Claude Bernard University Lyon 1 & Centre national de la recherche scientifique.
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The ade4 Package: Implementing the Duality Diagram for Ecologists
TL;DR: The theory of the duality diagram is presented and its implementation in ade4 is discussed, which follows the tradition of the French school of "Analyse des Donnees" and is based on the use of theDuality diagram.
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Revealing cryptic spatial patterns in genetic variability by a new multivariate method
TL;DR: This paper proposes a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations, and shows that sPCA performed better than PCA to reveal spatial genetic patterns.
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Community ecology in the age of multivariate multiscale spatial analysis
Stéphane Dray,Raphaël Pélissier,Raphaël Pélissier,Pierre Couteron,Marie-Josée Fortin,Pierre Legendre,Pedro R. Peres-Neto,Edwige Bellier,Roger Bivand,F. G. Blanchet,M. De Caceres,Anne-Béatrice Dufour,Einar Heegaard,Thibaut Jombart,Thibaut Jombart,François Munoz,Jari Oksanen,Jean Thioulouse,Helene H. Wagner +18 more
TL;DR: In this paper, the authors suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis.
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On the challenge of treating various types of variables: application for improving the measurement of functional diversity
TL;DR: It is proved Gower's distance can be extended to include new types of data and an evaluation of the real contribution of each variable to the mixed distance is proposed, concluding that such a generalized index will be crucial for analyzing functional diversity at small and large scales.