Title: Population Assignment using Genetic, Non-Genetic or Integrated
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@gmail.com>
Description: 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.
Suggests: gtable, iterators, klaR, stringi, knitr, rmarkdown

Suggests: gtable, iterators, klaR, stringi, knitr, rmarkdown, testthat

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
Description: 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.

Title: Infer Geographic Origin from Isotopic Data
Description: Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et. al (2014) <doi:10.1111/2041-210X.12229>.
Depends: raster, sp, rgdal, maptools, R (>= 2.10)

Description: Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.