DA: Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), 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.

Version: 1.2.0
Depends: R (≥ 3.5)
Imports: adegenet, lfda, MASS, kernlab, klaR, plotly, rARPACK, grDevices, stats, utils
Suggests: knitr, testthat, rmarkdown
Published: 2021-07-12
Author: Xinghu Qin ORCID iD [aut, cre, cph]
Maintainer: Xinghu Qin <qinxinghu at gmail.com>
BugReports: https://github.com/xinghuq/DA/issues
License: GPL-3
URL: https://xinghuq.github.io/DA/index.html
NeedsCompilation: no
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: DA results


Reference manual: DA.pdf
Vignettes: Instruction for Package DA


Package source: DA_1.2.0.tar.gz
Windows binaries: r-devel: DA_1.2.0.zip, r-release: DA_1.2.0.zip, r-oldrel: DA_1.2.0.zip
macOS binaries: r-release (arm64): DA_1.2.0.tgz, r-release (x86_64): DA_1.2.0.tgz, r-oldrel: DA_1.2.0.tgz


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