assignPOP: Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine Learning Framework

Use Monte-Carlo and K-fold cross-validation coupled with machine-learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.

Version: 1.1.4
Depends: R (≥ 2.3.2)
Imports: caret, doParallel, e1071, foreach, ggplot2, MASS, parallel, randomForest, reshape2, stringr, tree
Suggests: gtable, iterators, klaR, stringi, knitr, rmarkdown, testthat
Published: 2018-03-13
Author: Kuan-Yu (Alex) Chen [aut, cre], Elizabeth A. Marschall [aut], Michael G. Sovic [aut], Anthony C. Fries [aut], H. Lisle Gibbs [aut], Stuart A. Ludsin [aut]
Maintainer: Kuan-Yu (Alex) Chen <alexkychen at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: assignPOP results


Reference manual: assignPOP.pdf
Package source: assignPOP_1.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: assignPOP_1.1.4.tgz
OS X Mavericks binaries: r-oldrel: assignPOP_1.1.3.tgz
Old sources: assignPOP archive


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