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: 1.1.2
Depends: R (≥ 2.3.2)
Imports: caret, doParallel, e1071, foreach, ggplot2, MASS, parallel, randomForest, reshape2, stringr, tree
Suggests: gtable, iterators, klaR, stringi, knitr, rmarkdown
Published: 2017-05-16
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 gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/alexkychen/assignPOP, http://alexkychen.github.io/assignPOP/
NeedsCompilation: no
Materials: README
CRAN checks: assignPOP results

Downloads:

Reference manual: assignPOP.pdf
Package source: assignPOP_1.1.2.tar.gz
Windows binaries: r-devel: assignPOP_1.1.2.zip, r-release: assignPOP_1.1.2.zip, r-oldrel: assignPOP_1.1.2.zip
OS X El Capitan binaries: r-release: assignPOP_1.1.2.tgz
OS X Mavericks binaries: r-oldrel: assignPOP_1.1.2.tgz
Old sources: assignPOP archive

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