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Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine-learning Framework


This R package helps perform population assignment and infer population structure using a machine-learning framework. It employs supervised machine-learning methods to evaluate the discriminatory power of your data collected from source populations, and is able to analyze large genetic, non-genetic, or integrated (genetic plus non-genetic) data sets. This framework is designed for solving the upward bias issue discussed in previous studies. Main features are listed as follows.

Install assignPOP

You can install the released version from CRAN or the up-to-date version from this Github respository.

Note: When you install the package from Github, you may need to install additional packages before the assignPOP can be successfully installed. Follow the hints that R provided and then re-run install_github("alexkychen/assignPOP").

Package tutorial

Please visit our tutorial website for more infomration *

What’s new

Changes in ver. 1.1.6 (2019.6.8) - fix multiprocess issue in assign.kfold function

Changes in ver. 1.1.5 (2018.3.23) - Update assign.MC & assign.kfold to detect pop size and train.inds/k.fold setting - Update accuracy.MC & assign.matrix to handle test individuals not from every pop - Slightly modify levels method in accuracy.kfold - fix bugs in accuracy.plot for K-fold results - fix membership.plot title positioning and set text size to default


Changes in ver. 1.1.4 (2018.3.8) - Fix missing assign.matrix function

Changes in ver. 1.1.3 (2017.6.15) - Add unit tests (using package testthat)

Changes in ver. 1.1.2 (2017.5.13) - Change function name read.genpop to read.Genepop; Add function read.Structure. - Update read.genpop function, now can read haploid data

Cite this package

Chen K-Y, Marschall EA, Sovic MG, Fries AC, Gibbs HL, Ludsin SA. assignPOP: An R package for population assignment using genetic, non-genetic, or integrated data in a machine-learning framework. Methods in Ecology and Evolution. 2018;9:439–446.

Previous version

Previous packages can be found and downloaded at the releases page