Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, ), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.


Reference manual

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1.2.0 by Xinghu Qin, 3 months ago


Report a bug at https://github.com/xinghuq/DA/issues

Browse source code at https://github.com/cran/DA

Authors: Xinghu Qin [aut, cre, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports adegenet, lfda, MASS, kernlab, klaR, plotly, rARPACK, grDevices, stats, utils

Suggests knitr, testthat, rmarkdown

System requirements: GNU make

See at CRAN