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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: | 1.3.0 |
| Depends: | R (≥ 2.3.2) |
| Imports: | caret, doParallel, e1071, foreach, ggplot2, MASS, parallel, randomForest, reshape2, stringr, tree, rlang |
| Suggests: | gtable, iterators, klaR, stringi, knitr, rmarkdown, testthat |
| Published: | 2024-03-13 |
| 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 gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/alexkychen/assignPOP |
| NeedsCompilation: | no |
| CRAN checks: | assignPOP results |
| Reference manual: | assignPOP.pdf |
| Package source: | assignPOP_1.3.0.tar.gz |
| Windows binaries: | r-devel: assignPOP_1.3.0.zip, r-release: assignPOP_1.3.0.zip, r-oldrel: assignPOP_1.3.0.zip |
| macOS binaries: | r-release (arm64): assignPOP_1.3.0.tgz, r-oldrel (arm64): assignPOP_1.3.0.tgz, r-release (x86_64): assignPOP_1.3.0.tgz, r-oldrel (x86_64): assignPOP_1.3.0.tgz |
| Old sources: | assignPOP archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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