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

Perform population assignment using a machine learning framework. It employs supervised machine learning methods to evaluate the discriminatory power of your known data set, and is capable of analyzing large genetic, non-genetic, or integrated (genetic plus non-genetic) data sets. This framework is also designed for solving the upwardly biased issue that was discussed in previous studies.

Version: 1.1.1
Depends: R (≥ 2.3.2)
Imports: caret, doParallel, e1071, foreach, ggplot2, MASS, parallel, randomForest, reshape2, stringr, tree
Suggests: gtable, iterators, klaR, stringi
Published: 2016-12-29
Author: Kuan-Yu Alex Chen
Maintainer: Kuan-Yu Alex Chen <alexkychen at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/alexkychen/assignPOP
NeedsCompilation: no
Materials: README
CRAN checks: assignPOP results

Downloads:

Reference manual: assignPOP.pdf
Package source: assignPOP_1.1.1.tar.gz
Windows binaries: r-devel: assignPOP_1.1.1.zip, r-release: assignPOP_1.1.1.zip, r-oldrel: assignPOP_1.1.1.zip
OS X Mavericks binaries: r-release: assignPOP_1.1.1.tgz, r-oldrel: assignPOP_1.1.1.tgz

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